research for theoretical background

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Dissertation

What is a Theoretical Framework? | A Step-by-Step Guide

Published on 14 February 2020 by Shona McCombes . Revised on 10 October 2022.

A theoretical framework is a foundational review of existing theories that serves as a roadmap for developing the arguments you will use in your own work.

Theories are developed by researchers to explain phenomena, draw connections, and make predictions. In a theoretical framework, you explain the existing theories that support your research, showing that your work is grounded in established ideas.

In other words, your theoretical framework justifies and contextualises your later research, and it’s a crucial first step for your research paper , thesis, or dissertation . A well-rounded theoretical framework sets you up for success later on in your research and writing process.

Instantly correct all language mistakes in your text

Be assured that you'll submit flawless writing. Upload your document to correct all your mistakes.

Table of contents

Why do you need a theoretical framework, how to write a theoretical framework, structuring your theoretical framework, example of a theoretical framework, frequently asked questions about theoretical frameworks.

Before you start your own research, it’s crucial to familiarise yourself with the theories and models that other researchers have already developed. Your theoretical framework is your opportunity to present and explain what you’ve learned, situated within your future research topic.

There’s a good chance that many different theories about your topic already exist, especially if the topic is broad. In your theoretical framework, you will evaluate, compare, and select the most relevant ones.

By “framing” your research within a clearly defined field, you make the reader aware of the assumptions that inform your approach, showing the rationale behind your choices for later sections, like methodology and discussion . This part of your dissertation lays the foundations that will support your analysis, helping you interpret your results and make broader generalisations .

• In literature , a scholar using postmodernist literary theory would analyse The Great Gatsby differently than a scholar using Marxist literary theory.
• In psychology , a behaviourist approach to depression would involve different research methods and assumptions than a psychoanalytic approach.
• In economics , wealth inequality would be explained and interpreted differently based on a classical economics approach than based on a Keynesian economics one.

Prevent plagiarism, run a free check.

To create your own theoretical framework, you can follow these three steps:

• Identifying your key concepts
• Evaluating and explaining relevant theories
• Showing how your research fits into existing research

1. Identify your key concepts

The first step is to pick out the key terms from your problem statement and research questions . Concepts often have multiple definitions, so your theoretical framework should also clearly define what you mean by each term.

To investigate this problem, you have identified and plan to focus on the following problem statement, objective, and research questions:

Problem : Many online customers do not return to make subsequent purchases.

Objective : To increase the quantity of return customers.

Research question : How can the satisfaction of company X’s online customers be improved in order to increase the quantity of return customers?

2. Evaluate and explain relevant theories

By conducting a thorough literature review , you can determine how other researchers have defined these key concepts and drawn connections between them. As you write your theoretical framework, your aim is to compare and critically evaluate the approaches that different authors have taken.

After discussing different models and theories, you can establish the definitions that best fit your research and justify why. You can even combine theories from different fields to build your own unique framework if this better suits your topic.

Make sure to at least briefly mention each of the most important theories related to your key concepts. If there is a well-established theory that you don’t want to apply to your own research, explain why it isn’t suitable for your purposes.

3. Show how your research fits into existing research

Apart from summarising and discussing existing theories, your theoretical framework should show how your project will make use of these ideas and take them a step further.

You might aim to do one or more of the following:

• Test whether a theory holds in a specific, previously unexamined context
• Use an existing theory as a basis for interpreting your results
• Critique or challenge a theory
• Combine different theories in a new or unique way

A theoretical framework can sometimes be integrated into a literature review chapter , but it can also be included as its own chapter or section in your dissertation. As a rule of thumb, if your research involves dealing with a lot of complex theories, it’s a good idea to include a separate theoretical framework chapter.

There are no fixed rules for structuring your theoretical framework, but it’s best to double-check with your department or institution to make sure they don’t have any formatting guidelines. The most important thing is to create a clear, logical structure. There are a few ways to do this:

• Draw on your research questions, structuring each section around a question or key concept
• Organise by theory cluster
• Organise by date

As in all other parts of your research paper , thesis, or dissertation , make sure to properly cite your sources to avoid plagiarism .

To get a sense of what this part of your thesis or dissertation might look like, take a look at our full example .

While a theoretical framework describes the theoretical underpinnings of your work based on existing research, a conceptual framework allows you to draw your own conclusions, mapping out the variables you may use in your study and the interplay between them.

A literature review and a theoretical framework are not the same thing and cannot be used interchangeably. While a theoretical framework describes the theoretical underpinnings of your work, a literature review critically evaluates existing research relating to your topic. You’ll likely need both in your dissertation .

A theoretical framework can sometimes be integrated into a  literature review chapter , but it can also be included as its own chapter or section in your dissertation . As a rule of thumb, if your research involves dealing with a lot of complex theories, it’s a good idea to include a separate theoretical framework chapter.

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a dissertation , thesis, research paper , or proposal .

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2022, October 10). What is a Theoretical Framework? | A Step-by-Step Guide. Scribbr. Retrieved 31 May 2024, from https://www.scribbr.co.uk/thesis-dissertation/the-theoretical-framework/

Is this article helpful?

Shona McCombes

Other students also liked, what is a literature review | guide, template, & examples, how to write a results section | tips & examples, how to write a discussion section | tips & examples.

How To Write A Theoretical Research Paper – Tips & Examples

Writing a theoretical research paper might seem tough, but it’s a great way to share your ideas and discoveries. This guide will show you step-by-step how to plan, write, and share your thoughts through a strong research paper.

We’ll give you tips on how to build a clear framework and how to explain your thoughts clearly. You’ll also see examples that help make everything easier to understand.

Whether you’re a student or a researcher, these tips will help you write a paper that’s well-organized and full of good information. Let’s get started and learn how to create a great research paper!

How To Write A Theoretical Research Paper

What is theoretical research.

Theoretical research might sound daunting, but once you dive into its essence, it is simple.

In theoretical research, you focus on creating and exploring theories, models, and frameworks to understand and explain phenomena.

As you do this, you may not necessarily rely on direct observation or data collection.

In theoretical research, everything begins with a hypothesis. This hypothesis acts as a springboard for developing a complete theoretical framework. 

In the context of a research paper, especially in the social sciences, this type of research does not involve direct interaction with the subject of study. Instead, it focuses on the analysis of the research problem through a conceptual lens.

This lens is crafted from existing theory and literature review, which guides the research process meticulously.

The framework you’ve chosen essentially acts as a map, outlining the research questions and the methodology to explore these questions without the immediate need for empirical data.

One might wonder about the practical applications of such research. Theoretical models are not just abstract concepts; they are used to help develop practical solutions and interventions.

In psychology, a theoretical model might be applied to periods of significant social change to predict outcomes and suggest interventions.

Theoretical research can seem isolated from real-world applications, yet it serves as the foundation upon which more practical, or empirical research builds.

Without it, the structure of science would lack depth and fail to reach the heights of innovation and discovery that we see today.

Theoretical vs Empirical Research

Aside from theoretical research, theres also another type of research – empirical. Understanding the differences may help you significantly.

Theoretical research delves deep into concepts and abstracts. Here, you build your study around existing theories, crafting a theoretical framework that drives your inquiry.

In the social sciences, this could mean developing a new hypothesis on the dynamics of social change based on key social science theories from literature.

The theoretical framework serves not just as a guide but as a lens through which you examine your research problem. It’s crafted from thorough literature reviews and is often enriched by engaging with the philosophy of research.

This framework outlines key variables and the relationships among them, setting the stage for potential validation or challenge through empirical methods.

On the other hand, empirical research demands direct interaction with the subject matter through data collection. 

Empirical research seeks to validate the theories posited by your theoretical framework. Here, the focus shifts to practical applications and direct observations, providing concrete answers to your research questions.

Both research types are vital, each feeding into the other:

• Theoretical research frames the questions and potential explanations, while
• Empirical research tests these frameworks against reality. 

Together, they form the complete cycle of the research process, crucial for any scholarly research project.

Writing a theoretical research paper can seem daunting, but with the right approach, you can tackle this intellectually stimulating task with confidence. Here’s a step-by-step guide:

Step 1: Understand Your Research Problem

Your journey begins with a deep understanding of the research problem you are investigating.

This involves identifying the gaps in existing literature and pinpointing the areas that require further exploration. You may want to spend some time reading around, or use AI tools to help simplifying your reading process.

Engage with key theories and recent studies to sharpen your focus. The research problem forms the nucleus of your paper, guiding every subsequent step.

Step 2: Develop a Robust Theoretical Framework

Constructing a theoretical framework is crucial. This framework is the scaffolding of your research, supporting your entire study.

It consists of concepts and theories borrowed from existing literature and uniquely integrated to address your research problem.

Remember, a strong framework not only guides your analysis but also helps explain the relationships among key variables in your study.

Step 3: Literature Review

Your literature review should do more than summarize existing research; it should critically engage with current theories and frameworks, highlighting their strengths and weaknesses.

This section is not just a backdrop; it’s an active participant in shaping your research narrative. Organize it into a logical framework that systematically addresses the research questions posed by your study.

Literature review used to take a long time to complete. With the right tools however, things can be a lot easier:

Step 4: Outline Your Research Design

While theoretical research does not involve empirical data collection, the design of your research is still paramount. Detail the methods you use to construct your theoretical framework.

Discuss the “theory-building research methods” that you applied, such as conceptual analysis or deductive reasoning, which help clarify and test the theoretical assumptions of your study.

Step 5: Develop the Theory or Conceptual Framework

Here’s where you get to argue your point. Present your theoretical or conceptual contributions. Build upon previous research but introduce your innovative perspective.

Support each argument with robust reasoning, examples from pertinent research, and references to foundational texts.

This is also where you validate or challenge theoretical assumptions, demonstrating the novelty and relevance of your framework.

Step 6: Hypothetical Scenarios or Thought Experiments

Illustrate your concepts through hypothetical scenarios or thought experiments.

These are essential for demonstrating how your theoretical model applies to real-world situations or specific periods, even if your paper is purely conceptual.

This step is particularly engaging, as it transforms abstract concepts into tangible insights.

Step 7: Discussion

Analyse the implications of your theoretical developments. How do they: 

• Impact existing theories? or
• What do they mean for future research? 

This part of your paper is crucial for engaging with the scholarly community. It’s where you:

• interpret your findings,
• discuss their significance, and
• propose how they can guide future empirical or theoretical research.

Step 8: Craft Your Discussion Section

The discussion section is your chance to dive deep into the analysis of your theoretical propositions.

Evaluate the strengths and limitations of your framework, discuss its potential applications, and how it challenges or supports existing paradigms.

This section is not just a summary; it’s an insightful discourse that positions your research within the broader academic conversation.

Step 9: Concluding Thoughts

Summarize the key elements of your research, reinforcing the significance of your findings and their implications for further study.

Restate the research problem and reflect on how your work addresses it effectively.

Here, you tie all the sections together, reinforcing the coherence and impact of your theoretical investigation.

Step 10: Reference Section

No academic paper is complete without a thorough reference section. List all the sources you’ve cited throughout your paper.

This is crucial for academic integrity and allows other researchers to trace your intellectual journey. Make sure your referencing follows the specific style guide recommended by your field or university.

There are many AI tools that can help with references , so make sure you leverage technology to help you here.

By following these steps, you ensure that your theoretical research paper is not only structurally sound but also intellectually robust and poised to make a significant contribution to academic knowledge.

Remember, a well-crafted theoretical paper influences ongoing debates and paves the way for new inquiries and methodologies in the field.

Tips When Writing A Theoretical Research Paper

If you are looking to start writing your first theoretical research paper, here are some tips to help make the process easier:

Establish a Robust Theoretical Framework

Your research should start with a solid theoretical framework that consists of concepts and theories relevant to the research problem you are investigating.

If your topic concerns social media’s influence on mental health, you might integrate theories from psychology and communications. This framework not only shapes your study but also helps to interpret your findings.

Conduct a Thorough Literature Review

Dive deep into existing theory and scholarly research, examining studies that both support and contradict your hypothesis.

This comprehensive review not only furnishes you with a nuanced understanding of your topic but also positions your research within the broader academic conversation. 

Formulate Clear Research Questions

Theoretical research thrives on well-defined research questions. These questions should be rooted in the theoretical framework you’ve chosen and aim to explore the key variables and their relationships in your study.

Precision here will guide your entire research process, ensuring that every part of your paper contributes toward answering these questions.

Choose Appropriate Research Methods

Deciding on the right research methods is crucial. Ensure that the techniques you select align well with your theoretical assumptions and research questions, whether you opt for:

• qualitative research,
• intervention research, or
• a mixed methods approach, 

This alignment is necessary to gather valid and reliable data that supports or challenges your theoretical model.

Apply a Conceptual Framework If Needed

Sometimes, a single theoretical framework may not suffice, especially in interdisciplinary research. In such cases, developing a conceptual framework that integrates multiple theories could be more effective.

This approach was applied in a study about the educational split between Southern and Northern Sudan, where political science and educational theory provided a richer understanding of the regional disparities.

Discuss Methodology Transparently

When you write the discussion part of your paper, be transparent about your methodology. Explaining the meaning behind your choice of research design and how it’s used for your particular study adds credibility to your work.

It shows that your research methods and theoretical foundation are not just arbitrarily chosen but are thoughtfully aligned with the overall objectives of your research.

Interpret Results Within the Theoretical Framework

Finally, when presenting your results, always relate them back to the theoretical framework you set out with.

This not only reinforces the relevance of your findings within the academic field but also helps in validating or challenging theoretical assumptions. 

It’s here in the discussion section where you can engage deeply with the framework, proposing modifications or confirming its validity based on your findings.

Theoretical Research Paper: Not Rocket Science

Writing a theoretical research paper requires a meticulous blend of theory, critical thinking, and structured methodology.

By following the outlined steps, from developing a strong theoretical framework to effectively discussing your findings, you equip yourself with the tools to produce insightful and scholarly work.

Remember, the strength of your paper lies in how well you can integrate theory with your analytical insights, paving the way for further research and contributing to your field’s body of knowledge.

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.

Thank you for visiting Academia Insider.

We are here to help you navigate Academia as painlessly as possible. We are supported by our readers and by visiting you are helping us earn a small amount through ads and affiliate revenue - Thank you!

2024 © Academia Insider

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research Research Tools and Apps

Theoretical Research: Definition, Methods + Examples

Research is the careful study of a particular research problem or concern using the scientific method. A theory is essential for any research project because it gives it direction and helps prove or disprove something. Theoretical basis helps us figure out how things work and why we do certain things.

Theoretical research lets you examine and discuss a research object using philosophical ideas and abstract theoretical structures.

In theoretical research, you can’t look at the research object directly. With the help of research literature, your research aims to define and sketch out the chosen topic’s conceptual models, explanations, and structures.

LEARN ABOUT: Research Process Steps

This blog will cover theoretical research and why it is essential. In addition to that, we are going to go over some examples.

What is the theoretical research?

Theoretical research is the systematic examination of a set of beliefs and assumptions.

It aims to learn more about a subject and help us understand it better. The information gathered in this way is not used for anything in particular because this kind of research aims to learn more.

All professionals, like biologists, chemists, engineers, architects, philosophers, writers, sociologists, historians, etc., can do theoretical research. No matter what field you work in, theoretical research is the foundation for new ideas.

It tries to answer basic questions about people, which is why this kind of research is used in every field of knowledge.

For example , a researcher starts with the idea that we need to understand the world around us. To do this, he begins with a hypothesis and tests it through experiments that will help him develop new ideas. 

What is the theoretical framework?

A theoretical framework is a critical component in research that provides a structured foundation for investigating a specific topic or problem. It encompasses a set of interconnected theories, existing theories, and concepts that guide the entire research process. 

The theoretical framework introduces a comprehensive understanding of the subject matter. Also, the theoretical framework strengthens the research’s validity and specifies the key elements that will be explored. Furthermore, it connects different ideas and theories, forming a cohesive structure that underpins the research endeavor.

A complete theoretical framework consists of a network of theories, existing theories, and concepts that collectively shape the direction of a research study. 

The theoretical framework is the fundamental principle that will be explored, strengthens the research’s credibility by aligning it with established knowledge, specifies the variables under investigation, and connects different aspects of the research to create a unified approach.

Theoretical frameworks are the intellectual scaffolding upon which the research is constructed. It is the lens through which researchers view their subject, guiding their choice of methodologies, data collection, analysis, and interpretation. By incorporating existing theory, and established concepts, a theoretical framework not only grounds the research but also provides a coherent roadmap for exploring the intricacies of the chosen topic.

Benefits of theoretical research

Theoretical research yields a wealth of benefits across various fields, from social sciences to human resource development and political science. Here’s a breakdown of these benefits while incorporating the requested topics:

Predictive power

Theoretical models are the cornerstone of theoretical research. They grant us predictive power, enabling us to forecast intricate behaviors within complex systems, like societal interactions. In political science, for instance, a theoretical model helps anticipate potential outcomes of policy changes.

Understanding human behavior

Drawing from key social science theories, it assists us in deciphering human behavior and societal dynamics. For instance, in the context of human resource development, theories related to motivation and psychology provide insights into how to effectively manage a diverse workforce.

Optimizing workforce

In the realm of human resource development, insights gleaned from theoretical research, along with the research methods knowledge base, help create targeted training programs. By understanding various learning methodologies and psychological factors, organizations can optimize workforce training for better results.

Building on foundations

It doesn’t exist in isolation; it builds upon existing theories. For instance, within the human resource development handbook, theoretical research expands established concepts, refining their applicability to contemporary organizational challenges.

Ethical policy formulation

Within political science, theoretical research isn’t confined to governance structures. It extends to ethical considerations, aiding policymakers in creating policies that balance the collective good with individual rights, ensuring just and fair governance. 

Rigorous investigations

Theoretical research underscores the importance of research methods knowledge base. This knowledge equips researchers in theory-building research methods and other fields to design robust research methodologies, yielding accurate data and credible insights.

Long-term impact

Theoretical research leaves a lasting impact. The theoretical models and insights from key social science theories provide enduring frameworks for subsequent research, contributing to the cumulative growth of knowledge in these fields.

Innovation and practical applications

It doesn’t merely remain theoretical. It inspires innovation and practical applications. By merging insights from diverse theories and fields, practitioners in human resource development devise innovative strategies to foster employee growth and well-being.

Theoretical research method

Researchers follow so many methods when doing research. There are two types of theoretical research methods.

• Scientific methods
• Social science method 

Let’s explore them below:

Scientific method

Scientific methods have some important points that you should know. Let’s figure them out below:

• Observation: Any part you want to explain can be found through observation. It helps define the area of research.
• Hypothesis: The hypothesis is the idea put into words, which helps us figure out what we see.
• Experimentation: Hypotheses are tested through experiments to see if they are true. These experiments are different for each research.
• Theory: When we create a theory, we do it because we believe it will explain hypotheses of higher probability.
• Conclusions: Conclusions are the learnings we derive from our investigation.

Social science methods

There are different methods for social science theoretical research. It consists of polls, documentation, and statistical analysis.

• Polls: It is a process whereby the researcher uses a topic-specific questionnaire to gather data. No changes are made to the environment or the phenomenon where the polls are conducted to get the most accurate results. QuestionPro live polls are a great way to get live audiences involved and engaged.
• Documentation: Documentation is a helpful and valuable technique that helps the researcher learn more about the subject. It means visiting libraries or other specialized places, like documentation centers, to look at the existing bibliography. With the documentation, you can find out what came before the investigated topic and what other investigations have found. This step is important because it shows whether or not similar investigations have been done before and what the results were.
• Statistic analysis : Statistics is a branch of math that looks at random events and differences. It follows the rules that are established by probability. It’s used a lot in sociology and language research. 

Examples of theoretical research

We talked about theoretical study methods in the previous part. We’ll give you some examples to help you understand it better.

Example 1: Theoretical research into the health benefits of hemp

The plant’s active principles are extracted and evaluated, and by studying their components, it is possible to determine what they contain and whether they can potentially serve as a medication.

Example 2: Linguistics research

Investigate to determine how many people in the Basque Country speak Basque. Surveys can be used to determine the number of native Basque speakers and those who speak Basque as a second language.

Example 3: Philosophical research

Research politics and ethics as they are presented in the writings of Hanna Arendt from a theoretical perspective.

LEARN ABOUT: 12 Best Tools for Researchers

From our above discussion, we learned about theoretical research and its methods and gave some examples. It explains things and leads to more knowledge for the sake of knowledge. This kind of research tries to find out more about a thing or an idea, but the results may take time to be helpful in the real world. 

This research is sometimes called basic research. Theoretical research is an important process that gives researchers valuable data with insight.

QuestionPro is a strong platform for managing your data. You can conduct simple surveys to more complex research using QuestionPro survey software.

At QuestionPro, we give researchers tools for collecting data, such as our survey software and a library of insights for any long-term study. Contact our expert team to find out more about it.

FREE TRIAL         LEARN MORE

MORE LIKE THIS

Raked Weighting: A Key Tool for Accurate Survey Results

May 31, 2024

Top 8 Data Trends to Understand the Future of Data

May 30, 2024

Top 12 Interactive Presentation Software to Engage Your User

May 29, 2024

Trend Report: Guide for Market Dynamics & Strategic Analysis

Other categories.

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Uncategorized
• Video Learning Series
• What’s Coming Up
• Workforce Intelligence

The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Introduction
• How do you prepare for qualitative research?

Common theoretical perspectives

Choosing the right perspective, methodological implications, future directions.

• Theoretical framework
• Literature reviews
• Research question
• Conceptual framework
• Conceptual vs. theoretical framework
• Data collection
• Qualitative research methods
• Focus groups
• Observational research
• Case studies
• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

Theoretical perspective

Prior to any qualitative research design, qualitative researchers can choose a theoretical perspective to apply to their study. Qualitative research needs grounding in a specific epistemology to answer research questions and generate the appropriate research findings. The theoretical perspective guides the creation of theoretical frameworks through which to view the research question, and it can inform the methodology , data collection , analysis , and interpretation of the findings.

Rationale for a theoretical perspective

Let's explore a few reasons why choosing a theoretical perspective is important.

Provides a framework for understanding the phenomenon

A theoretical perspective helps to provide a framework for understanding the phenomenon under investigation. It can help the researcher to identify relevant concepts and variables and to understand how they might be related to each other.

Choosing a theoretical perspective can be vital for psychological qualitative research, for example, as it shapes the way a researcher approaches and comprehends various mental processes and human behaviors under study. A well-chosen theoretical perspective lays the foundation for the research, informing the selection of research questions , methodology , data collection , and data analysis techniques. Furthermore, it situates the study within the larger context of psychological theories and understanding, ensuring that the research contributes meaningfully to the existing body of knowledge. By adopting an appropriate theoretical perspective, such as cognitive, behavioral, or psychodynamic, the researcher can address potential biases and assumptions, thereby enhancing the credibility, validity, and reliability of the findings in the field of psychology.

Shapes research questions

A theoretical perspective can help to shape the research questions , hypotheses , and objectives that the researcher wants to investigate. The questions that are asked will depend on the theoretical perspective and assumptions being made about the phenomenon.

Guides data collection and data analysis

The theoretical perspective can guide the collection and analysis of data by informing the qualitative methods used to collect data, such as interviews , focus groups , or observations . It can also inform the types of data that are collected and the way in which the data are analyzed.

Increases the credibility of the research

Choosing a theoretical perspective can help to increase the credibility of the research by demonstrating that the researcher has thought carefully about the phenomenon being studied and has situated the study within a relevant theoretical framework. This can increase the rigor of the research and make it more likely to be accepted and understood by the academic community.

Overall, choosing a theoretical perspective is important because it helps the researcher to situate the study within a broader context and provides a framework for understanding the phenomenon under investigation.

In qualitative research, theoretical perspectives play a crucial role in guiding the research process and interpreting the findings. This section will provide a brief overview of the major theoretical perspectives in qualitative research, which can be helpful for emerging researchers.

Constructivism

Constructivism is a philosophical and methodological approach that emphasizes the central role of human cognition in constructing knowledge and understanding the world. In qualitative research, constructivism provides a framework for exploring how individuals construct meaning from their experiences, interactions, and the social context in which they live. Constructivism can help researchers adapt to the dynamic nature of human experience and meaning-making.

Interpretivism

Interpretivism is a philosophical and methodological approach that emphasizes the importance of understanding the social world through the subjective experiences and interpretations of individuals. In qualitative research , interpretivism provides a framework for exploring the meanings, beliefs, and values that guide people's actions and decision-making in various social contexts.

Symbolic interactionism

A symbolic interactionist perspective seeks to explain social phenomena and human behavior through the effects that social interaction has on our way of thinking and how we understand the world around us. In this sense, lived experience and subjective perception are key to understanding knowledge and have profound influences on the social structure of groups and cultures.

Critical theory

Critical theory seeks to understand and challenge power structures and social inequalities with the goal of promoting social change. Researchers adopting this perspective aim to expose the underlying causes of social problems and empower marginalized groups. They often focus on issues related to race, gender, class, and other forms of social and economic inequality. Conflict perspective, symbolic violence, and hermeneutical injustice are all central to critical theory as they focus on power inequities and their root causes.

Conflict theory

Conflict theory is a perspective rooted in sociological theory that examines society through the lens of power, inequality, and social conflict. It posits that society is characterized by ongoing struggles for resources and control among different groups. Originating from Karl Marx's work, conflict theory emphasizes the social and economic disparities that lead to tensions and conflicts. In qualitative research, conflict theory provides a framework to understand power dynamics, oppression, and social inequality. It prompts researchers to investigate how conflicts shape social interactions, institutions, and norms. Adopting a conflict theory perspective allows qualitative researchers to illuminate power struggles and social injustices, contributing to efforts for social change.

Critical race theory

While scholars like Foucault developed critical theory to understand and explain social institutions and power in a general sense, critical race theory looks at power inequities primarily within the context of race. Critical race theorists seek to expose and challenge the ways in which racism operates in society and to promote racial justice and equality. Critical race theory has been used to analyze a range of different areas, including education, criminal justice, and housing.

Feminist theory

Feminist theory aims to understand and challenge gender-based power inequalities and promote the social, political, and economic equality of all genders. This perspective emphasizes the need to understand the experiences of women and other marginalized genders, as well as the ways in which gender intersects with other social categories like race and class.

Postmodernism

Postmodernism questions the existence of objective truths and universal narratives, arguing that knowledge is always contextual and contingent. Researchers adopting a postmodern perspective often emphasize the plurality of perspectives, the fragmentation of identities, and the instability of meaning. This approach encourages researchers to critically examine their own assumptions and the power dynamics that influence the research process.

Post-structuralism

Post-structuralism critiques the idea that there are fixed, stable structures that determine meaning and reality. Instead, this perspective emphasizes the complexity, fluidity, and multiple interpretations of social phenomena by challenging theoretical assumptions about the world around us. Researchers adopting a post-structuralist approach often focus on the role of language, discourse, and power in shaping our understanding of the world.

Grounded theory

Grounded theory is an inductive research approach that aims to generate theories grounded in empirical data. Researchers using this perspective collect and analyze data concurrently, allowing the emerging theory to guide the research process. This approach emphasizes the development of conceptual categories and the relationships between them rather than focusing on the testing of pre-existing theories.

These theoretical perspectives are not mutually exclusive and can be combined or adapted to suit the specific research context and goals. By understanding and choosing an appropriate theoretical perspective, researchers can ensure a more coherent and rigorous research process, as well as more meaningful and valid interpretations of their data .

Turn analysis into theory with ATLAS.ti

Tools that turn your data into insights are just a few clicks away with a free trial of ATLAS.ti.

Selecting an appropriate theoretical perspective is a crucial step in conducting qualitative research, as it shapes the researcher's approach to data collection , analysis , and interpretation . The choice of a theoretical perspective should be informed by the research question , the study's goals, and the researcher's epistemological and ontological assumptions. Here are some key factors to consider when choosing a theoretical perspective for qualitative research:

- Research question and objectives: The research question and objectives should guide the choice of a theoretical perspective. Consider which perspective best aligns with the goals of the study and is most likely to help you address the research question effectively. For example, if your study aims to explore power dynamics and social inequalities, a critical theory or feminist theory perspective may be appropriate.

- Epistemological and ontological assumptions: Your epistemological (how we know what we know) and ontological (the nature of reality) assumptions influence your choice of a theoretical perspective. Reflect on your beliefs about the nature of knowledge and reality, and consider which perspective aligns with these assumptions and the goals of your study. For instance, if you approach knowledge as something that is subjective and contingent, you may lean towards a constructivist, interpretivist, or postmodernist perspective.

- Theoretical and personal interests: Your own theoretical interests and personal experiences may also influence your choice of a theoretical perspective. Consider your background, academic discipline, and areas of expertise, as well as your personal values, experiences, and interests. Choosing a perspective that resonates with your own interests and experiences can help ensure a more engaged and passionate research process.

- Study population and context: The study population and context should also be taken into account when selecting a theoretical perspective. Consider the characteristics of your participants, the setting of the study, and the broader social, cultural, and historical context in which the research takes place. Some perspectives may be more appropriate for specific populations, settings, or contexts, while others may be more applicable across a range of situations.

- Methodological implications: The choice of a theoretical perspective has implications for the research methods and techniques you will employ. Consider which perspective best aligns with the methodological approach you plan to use and is likely to yield the most valuable insights. For example, if you plan to use narrative inquiry or discourse analysis , a postmodernist or post-structuralist perspective may be suitable.

- Flexibility and openness: While it is essential to choose a theoretical perspective that aligns with your research question, goals, and assumptions, it is also important to remain flexible and open to new insights and perspectives that may emerge during the research process. Be prepared to revisit and refine your theoretical perspective as you collect and analyze data , incorporating new ideas and perspectives as appropriate.

By carefully considering all these factors, researchers can select a perspective that will guide and enrich their research process and findings. To understand how these different aspects can be aligned, it is also helpful to pay attention to these when reading other studies published in your area.

As mentioned previously, choosing from the various theoretical perspectives can guide your research inquiry and study design. Let's look more closely at the influences that the right perspective can have on your research methodology.

Theoretical perspectives hold various implications for sampling strategies. Each prioritizes different considerations when selecting participants or cases for study. Researchers should align their sampling strategy with the chosen theoretical perspective to ensure that the selected participants or cases are relevant to the research focus and theoretical framework . For instance, a critical theory perspective may prioritize purposeful sampling to include marginalized or underrepresented voices, while a phenomenological perspective may prioritize maximum variation sampling to capture diverse experiences.

Data collection methods

The theoretical perspective adopted influences the selection of data collection methods in qualitative research. Different perspectives emphasize different types of data and data collection techniques. Researchers need to consider how their chosen theoretical perspective guides the selection and application of appropriate data collection methods to effectively address their research questions . For example, an ethnographic perspective may prioritize participant observation , interviews , and field notes to capture rich contextual data, while a feminist perspective may emphasize the use of narratives and life histories to explore power dynamics .

Data analysis techniques

Data analysis is also impacted by the chosen theoretical perspective. For example, theoretical perspectives inform the selection of analytical frameworks, coding schemes , and interpretation strategies. Researchers should align their data analysis techniques with the theoretical perspective to ensure that the analysis captures the nuances and insights relevant to the research questions. For instance, a poststructuralist perspective may employ discourse analysis to deconstruct power relations and discursive formations, while a grounded theory perspective may employ constant comparative analysis to develop theoretical categories.

Interpretation and findings

Theoretical perspectives shape the interpretation of findings and the construction of knowledge in qualitative research. Researchers must consider how their chosen theoretical perspective guides the interpretation of findings and contributes to the generation of meaningful and contextually situated knowledge. Each perspective offers different lenses through which researchers interpret their data and generate insights. For example, a postcolonial perspective may draw attention to the colonial legacies and power imbalances embedded in the research findings. In contrast, a phenomenological perspective may focus on the lived experiences and subjective meanings.

It is crucial for researchers to recognize that methodological implications are not rigid prescriptions but flexible guidelines. Researchers should adapt and refine their methodological choices based on the specific research context, research questions, and theoretical perspective, considering the strengths and limitations of each approach.

Emerging trends and advancements in theoretical perspectives offer exciting opportunities for researchers to innovate and expand the boundaries of qualitative research . These future directions push the boundaries of traditional theoretical perspectives, exploring new avenues of inquiry and addressing contemporary challenges. This section presents ideas for potential innovations in theoretical perspectives, highlighting areas where qualitative researchers can make significant contributions.

Intersectionality and complex systems thinking

Incorporating intersectionality and complex systems thinking into theoretical perspectives can enhance the understanding of multifaceted social phenomena. Intersectionality recognizes the interconnections between various social categories such as race, gender, class, and sexuality, acknowledging the unique experiences and oppressions that result from their overlapping effects. Complex systems thinking explores the dynamic relationships and feedback loops that shape social systems. By integrating these perspectives, researchers can develop a more nuanced understanding of the complexities and interdependencies within social phenomena.

Global and transnational perspectives

With increasing globalization, researchers can explore theoretical perspectives that transcend national boundaries. Global and transnational perspectives emphasize the interconnectedness of societies, cultures, and institutions across the globe. These perspectives can shed light on global social issues, transnational identities, and the effects of global processes on local contexts. Researchers can adopt theoretical lenses that capture the complexities of global interdependencies, migration, diaspora, and cross-cultural encounters, facilitating a more comprehensive understanding of contemporary social dynamics.

Digital and technological transformations

Advancements in digital technologies have transformed social interactions, communication, and access to information. Researchers can explore theoretical perspectives that incorporate digital and technological dimensions. This includes studying the impact of digital platforms, social media , virtual communities, and artificial intelligence on social structures, power dynamics, identity formation, and social movements. By integrating digital and technological aspects into theoretical frameworks, researchers can better understand the evolving nature of social life in the digital age.

Environmental and ecological perspectives

Given the pressing environmental challenges, researchers can adopt theoretical perspectives that place emphasis on the environment and ecological systems. Environmental and ecological perspectives consider the intricate relationships between humans, their environments, and the natural world. By integrating these perspectives, researchers can explore the sociocultural dimensions of environmental issues, climate change, sustainability, and the interactions between human societies and the natural environment. These perspectives encourage a holistic understanding of social and ecological systems, paving the way for innovative research that addresses urgent environmental concerns.

Critical data studies and ethical implications

As data collection and analysis become increasingly prevalent in society, researchers can engage with critical data studies to examine the societal and ethical implications of data practices. Critical data studies explore issues such as data surveillance, privacy , algorithmic bias, and the power dynamics embedded in data-driven decision-making. By integrating critical perspectives into theoretical frameworks, researchers can investigate how data practices shape social structures, inequalities, and the lived experiences of individuals and communities. This approach encourages reflexivity and ethical considerations in the collection, analysis, and dissemination of data.

Participatory and community-based approaches

To foster more inclusive and empowering research processes, researchers can embrace participatory and community-based approaches within theoretical perspectives. These approaches involve engaging participants and communities as active collaborators, valuing their lived experiences and knowledge. By integrating participatory and community-based practices, researchers can address power imbalances, amplify marginalized voices, and co-create knowledge that is meaningful and relevant to the communities under study. This empowers participants as agents of change and ensures research findings contribute to tangible positive outcomes.

While this discussion may seem to go far afield, embracing future directions in theoretical perspectives offers opportunities for qualitative researchers to innovate and contribute to advancing the field. By incorporating approaches such as intersectionality, complex systems thinking, global perspectives, digital transformations, environmental considerations, critical data studies, or participatory research, researchers can explore new dimensions of social phenomena, address contemporary challenges, and engage in socially relevant research.

These innovations allow for a more comprehensive understanding of complex social dynamics, foster ethical and inclusive research practices, and generate knowledge that can contribute to positive societal change. As the research landscape continues to evolve, qualitative researchers can seize these future directions to push the boundaries of theoretical perspectives, deepen our understanding of the social world, and ultimately make meaningful contributions to academic scholarship and societal well-being.

Data analysis made easier with ATLAS.ti

Any field, any inquiry, any theory - ATLAS.ti supports all your research. See how with a free trial.

• Library databases
• Library website

Theories and Frameworks: Discover Theories

Where to start.

There are many ways to find theories that are relevant to your coursework and research.

To look for a theory for a discussion post or assignment, these strategies are a good place to start.

• Try searching encyclopedias and books for the concept or subject area you're interested .  Review the entries and look for a section on theories.
• Another strategy is to search scholarly articles on your topic to see what theories are being used in the literature.

Dissertation and Doctoral Studies

Finding a theory for a capstone is a more involved process because the theory must align with your specific research problem. You may find it necessary to use most or all of the search strategies and tools in this guide. 

• A good place to start is by searching your topic in completed dissertations since theory and its alignment with the research problem is often discussed in more depth than in a published research article.
• Encyclopedias, books, and scholarly articles are also useful sources.

Encyclopedias & books

Encyclopedias and handbooks.

Scholarly encyclopedias and handbooks are great places to find information on theories. The Library has two encyclopedia databases: Sage Knowledge and Gale eBooks. 

Try the following search strategies when searching encyclopedias and handbooks. Review the book's table of contents or index for sections on theory.

• Search for the subject area you're interested in such as  education
• Search for the specific concept you're interested in such as  mentoring
• Experiment with different search terms such as  mentoring  or  employee mentoring  or  mentoring theory

The Library also has scholarly books that are available in full text and are another great resource. Search your topic as you would for scholarly articles (one idea/concept per search box). Use the "Advanced search" link and enter your topic in the first search box; in the second search box, experiment with adding  theory OR theories.  

• SAGE Knowledge This database contains encyclopedias and handbooks in over 20 different subject areas.
• Gale eBooks The collection has encyclopedias and specialized reference resources.
• Walden Library Books Find books available in the Walden Library.

Scholarly articles

Searching your topic in the scholarly literature will give you an idea of what theories have been used in the research related to your topic. Take notes on the theories being used so you can investigate them later in more depth. 

Use the Library databases to research theories related to your topic. EXAMPLE: Search articles on mentoring new teachers.  

• Use the drop-down menu to choose the subject related to your topic. EXAMPLE: Education  
• Click on the databases drop-down menu to choose a database related to your search. EXAMPLE: in the Education Databases drop-down menu, choose  Education Source . You may need to log in with your Walden email and password.

• Review the results and browse the subject terms under each article in the results list as well as the article's abstract to identify articles of interest. Browse those articles for potential theories by scanning the introduction, literature review, and sections titled theoretical or conceptual framework.

Dissertations

Similar to searching scholarly articles, searching completed dissertations and doctoral studies related to your topic can help you locate theories that may align with your own research. You can also review their references to see what theories are being used in those articles. Search your topic as you would for scholarly articles (one idea/concept per search box).

• Dissertations & Theses @ Walden University The database contains full text of dissertations and theses written by Walden students.
• ProQuest Dissertations & Theses Global The Dissertations and Theses database gives you full text access to over 3 million dissertations and theses from schools and universities around the world, including Walden dissertations. You can choose to search either all the dissertations and theses, or just those created at Walden.

Search dissertations or doctoral studies by degree:

• Quick Answer: How do I find Walden Ph.D. dissertations?
• Quick Answer: What degree codes are used to find completed Walden capstones or dissertations?

Google Scholar

Google Scholar is another option for exploring theories since it searches broadly across publisher's websites, repositories, and other libraries. Remember, you cannot limit to peer review or full text. By using the Walden Library's pre-configured Google Scholar search, you can quickly see which articles are available in the Walden Library.

For example, a theory search in Google Scholar for mentoring first year teachers might look like this: 

• Click  the  Search  button.
• Review the results list for relevant articles. Search terms will be bolded. Articles available online or in the Walden Library will have a link next to the article. Learn about accessing full text articles through the Find @ Walden button.

Learn more about searching Google Scholar

• Google Scholar Library Guide
• Previous Page: Introduction
• Next Page: Learn About a Theory
• Office of Student Disability Services

Walden Resources

Departments.

• Academic Residencies
• Academic Skills
• Career Planning and Development
• Customer Care Team
• Field Experience
• Military Services
• Student Success Advising
• Writing Skills

Centers and Offices

• Center for Social Change
• Office of Academic Support and Instructional Services
• Office of Degree Acceleration
• Office of Research and Doctoral Services
• Office of Student Affairs

Student Resources

• Doctoral Writing Assessment
• Form & Style Review
• Quick Answers
• ScholarWorks
• SKIL Courses and Workshops
• Walden Bookstore
• Walden Catalog & Student Handbook
• Student Safety/Title IX
• Legal & Consumer Information
• Website Terms and Conditions
• Cookie Policy
• Accessibility
• Accreditation
• State Authorization
• Net Price Calculator
• Contact Walden

Walden University is a member of Adtalem Global Education, Inc. www.adtalem.com Walden University is certified to operate by SCHEV © 2024 Walden University LLC. All rights reserved.

Organizing Academic Research Papers: Theoretical Framework

• Purpose of Guide
• Design Flaws to Avoid
• Glossary of Research Terms
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• Executive Summary
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tertiary Sources
• What Is Scholarly vs. Popular?
• Qualitative Methods
• Quantitative Methods
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Annotated Bibliography
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• How to Manage Group Projects
• Multiple Book Review Essay
• Reviewing Collected Essays
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Research Proposal
• Acknowledgements

Theories are formulated to explain, predict, and understand phenomena and, in many cases, to challenge and extend existing knowledge, within the limits of the critical bounding assumptions. The theoretical framework is the structure that can hold or support a theory of a research study. The theoretical framework introduces and describes the theory which explains why the research problem under study exists.

Importance of Theory

A theoretical framework consists of concepts, together with their definitions, and existing theory/theories that are used for your particular study. The theoretical framework must demonstrate an understanding of theories and concepts that are relevant to the topic of your  research paper and that will relate it to the broader fields of knowledge in the class you are taking.

The theoretical framework is not something that is found readily available in the literature . You must review course readings and pertinent research literature for theories and analytic models that are relevant to the research problem you are investigating. The selection of a theory should depend on its appropriateness, ease of application, and explanatory power.

The theoretical framework strengthens the study in the following ways .

• An explicit statement of  theoretical assumptions permits the reader to evaluate them critically.
• The theoretical framework connects the researcher to existing knowledge. Guided by a relevant theory, you are given a basis for your hypotheses and choice of research methods.
• Articulating the theoretical assumptions of a research study forces you to address questions of why and how. It permits you to move from simply describing a phenomenon observed to generalizing about various aspects of that phenomenon.
• Having a theory helps you to identify the limits to those generalizations. A theoretical framework specifies which key variables influence a phenomenon of interest. It alerts you to examine how those key variables might differ and under what circumstances.

By virtue of its application nature, good theory in the social sciences is of value precisely because it fulfills one primary purpose: to explain the meaning, nature, and challenges of a phenomenon, often experienced but unexplained in the world in which we live, so that we may use that knowledge and understanding to act in more informed and effective ways.

The Conceptual Framework. College of Education. Alabama State University; Drafting an Argument . Writing@CSU. Colorado State University; Trochim, William M.K. Philosophy of Research. Research Methods Knowledge Base. 2006.

Strategies for Developing the Theoretical Framework

I.  Developing the Framework

Here are some strategies to develop of an effective theoretical framework:

• Examine your thesis title and research problem . The research problem anchors your entire study and forms the basis from which you construct your theoretical framework.
• Brainstorm on what you consider to be the key variables in your research . Answer the question, what factors contribute to the presumed effect?
• Review related literature to find answers to your research question.
• List  the constructs and variables that might be relevant to your study. Group these variables into independent and dependent categories.
• Review the key social science theories that are introduced to you in your course readings and choose the theory or theories that can best explain the relationships between the key variables in your study [note the Writing Tip on this page].
• Discuss the assumptions or propositions of this theory and point out their relevance to your research.

A theoretical framework is used to limit the scope of the relevant data by focusing on specific variables and defining the specific viewpoint (framework) that the researcher will take in analyzing and interpreting the data to be gathered, understanding concepts and variables according to the given definitions, and building knowledge by validating or challenging theoretical assumptions.

II.  Purpose

Think of theories as the conceptual basis for understanding, analyzing, and designing ways to investigate relationships within social systems. To the end, the following roles served by a theory can help guide the development of your framework.*

• Means by which new research data can be interpreted and coded for future use,
• Response to new problems that have no previously identified solutions strategy,
• Means for identifying and defining research problems,
• Means for prescribing or evaluating solutions to research problems,
• Way of telling us that certain facts among the accumulated knowledge are important and which facts are not,
• Means of giving old data new interpretations and new meaning,
• Means by which to identify important new issues and prescribe the most critical research questions that need to be answered to maximize understanding of the issue,
• Means of providing members of a professional discipline with a common language and a frame of reference for defining boundaries of their profession, and
• Means to guide and inform research so that it can, in turn, guide research efforts and improve professional practice.

*Adapted from: Torraco, R. J. “Theory-Building Research Methods.” In Swanson R. A. and E. F. Holton III , editors. Human Resource Development Handbook: Linking Research and Practice . (San Francisco, CA: Berrett-Koehler, 1997): pp. 114-137; Sutton, Robert I. and Barry M. Staw. “What Theory is Not.” Administrative Science Quarterly 40 (September 1995): 371-384.

Structure and Writing Style

The theoretical framework may be rooted in a specific theory , in which case, you are expected to test the validity of an existing theory in relation to specific events, issues, or phenomena. Many social science research papers fit into this rubric. For example, Peripheral Realism theory, which categorizes perceived differences between nation-states as those that give orders, those that obey, and those that rebel, could be used as a means for understanding conflicted relationships among countries in Africa. A test of this theory could be the following: Does Peripheral Realism theory help explain intra-state actions, such as, the growing split between southern and northern Sudan that may likely lead to the creation of two nations?

However, you may not always be asked by your professor to test a specific theory in your paper, but to develop your own framework from which your analysis of the research problem is derived . Given this, it is perhaps easiest to understand the nature and function of a theoretical framework if it is viewed as the answer to two basic questions:

• What is the research problem/question? [e.g., "How should the individual and the state relate during periods of conflict?"]
• Why is your approach a feasible solution? [I could choose to test Instrumentalist or Circumstantialists models developed among Ethnic Conflict Theorists that rely upon socio-economic-political factors to explain individual-state relations and to apply this theoretical model to periods of war between nations].

The answers to these questions come from a thorough review of the literature and your course readings [summarized and analyzed in the next section of your paper] and the gaps in the research that emerge from the review process. With this in mind, a complete theoretical framework will likely not emerge until after you have completed a thorough review of the literature .

In writing this part of your research paper, keep in mind the following:

• Clearly describe the framework, concepts, models, or specific theories that underpin your study . This includes noting who the key theorists are in the field who have conducted research on the problem you are investigating and, when necessary, the historical context that supports the formulation of that theory. This latter element is particularly important if the theory is relatively unknown or it is borrowed from another discipline.
• Position your theoretical framework within a broader context of related frameworks , concepts, models, or theories . There will likely be several concepts, theories, or models that can be used to help develop a framework for understanding the research problem. Therefore, note why the framework you've chosen is the appropriate one.
• The present tense is used when writing about theory.
• You should make your theoretical assumptions as explicit as possible . Later, your discussion of methodology should be linked back to this theoretical framework.
• Don’t just take what the theory says as a given! Reality is never accurately represented in such a simplistic way; if you imply that it can be, you fundamentally distort a reader's ability to understand the findings that emerge. Given this, always note the limitiations of the theoretical framework you've chosen [i.e., what parts of the research problem require further investigation because the theory does not explain a certain phenomena].

The Conceptual Framework. College of Education. Alabama State University; Conceptual Framework: What Do You Think is Going On? College of Engineering. University of Michigan; Drafting an Argument . Writing@CSU. Colorado State University; Lynham, Susan A. “The General Method of Theory-Building Research in Applied Disciplines.” Advances in Developing Human Resources 4 (August 2002): 221-241; Tavallaei, Mehdi and Mansor Abu Talib. A General Perspective on the Role of Theory in Qualitative Research. Journal of International Social Research 3 (Spring 2010); Trochim, William M.K. Philosophy of Research. Research Methods Knowledge Base. 2006.

Writing Tip

Borrowing Theoretical Constructs from Elsewhere

A growing and increasingly important trend in the social sciences is to think about and attempt to understand specific research problems from an interdisciplinary perspective. One way to do this is to not rely exclusively on the theories you've read about in a particular class, but to think about how an issue might be informed by theories developed in other disciplines. For example, if you are a political science student studying the rhetorical strategies used by female incumbants in state legislature campaigns, theories about the use of language could be derived, not only from political science, but linguistics, communication studies, philosophy, psychology, and, in this particular case, feminist studies. Building theoretical frameworks based on the postulates and hypotheses developed in other disciplinary contexts can be both enlightening and an effective way to be fully engaged in the research topic.

Another Writing Tip

Don't Undertheorize!

Never leave the theory hanging out there in the Introduction never to be mentioned again. Undertheorizing weakens your paper. The theoretical framework you introduce should guide your study throughout the paper. Be sure to always connect theory to the analysis and to explain in the discussion part of your paper how the theoretical framework you chose fit the research problem, or if appropriate, was inadequate in explaining the phenomenon you were investigating. In that case, don't be afraid to propose your own theory based on your findings.

Still Another Writing Tip

What's a Theory? What's a Hypothesis?

The terms theory and hypothesis are often used interchangeably in everyday use. However, the difference between them in scholarly research is important, particularly when using an experimental design. A theory is a well-established principle that has been developed to explain some aspect of the natural world. Theories arise from repeated observation and testing and incorporates facts, laws, predictions, and tested hypotheses that are widely accepted [e.g., rational choice theory; grounded theory].

A hypothesis is a specific, testable prediction about what you expect to happen in your study. For example, an experiment designed to look at the relationship between study habits and test anxiety might have a hypothesis that states, "We predict that students with better study habits will suffer less test anxiety." Unless your study is exploratory in nature, your hypothesis should always explain what you expect to happen during the course of your research.

The key distinctions are:

• A theory predicts events in a broad, general context;  a hypothesis makes a specific prediction about a specified set of circumstances.
• A theory has been extensively tested and is generally accepted among scholars; a hypothesis is a speculative guess that has yet to be tested.

Cherry, Kendra. Introduction to Research Methods: Theory and Hypothesis . About.com Psychology; Gezae, Michael et al. Welcome Presentation on Hypothesis . Slideshare presentation.

• << Previous: The Research Problem/Question
• Next: 5. The Literature Review >>
• Last Updated: Jul 18, 2023 11:58 AM
• URL: https://library.sacredheart.edu/c.php?g=29803
• QuickSearch
• Library Catalog
• Databases A-Z
• Publication Finder
• Course Reserves
• Citation Linker
• Digital Commons
• Our Website

Research Support

• Ask a Librarian
• Appointments
• Interlibrary Loan (ILL)
• Research Guides
• Databases by Subject
• Citation Help

Using the Library

• Reserve a Group Study Room
• Renew Books
• Honors Study Rooms
• Off-Campus Access
• Library Policies
• Library Technology

User Information

• Grad Students
• Online Students
• COVID-19 Updates
• Staff Directory
• News & Announcements
• Library Newsletter

My Accounts

• Interlibrary Loan
• Staff Site Login

FIND US ON  

• Privacy Policy

Home » Background of The Study – Examples and Writing Guide

Background of The Study – Examples and Writing Guide

Table of Contents

Background of The Study

Definition:

Background of the study refers to the context, circumstances, and history that led to the research problem or topic being studied. It provides the reader with a comprehensive understanding of the subject matter and the significance of the study.

The background of the study usually includes a discussion of the relevant literature, the gap in knowledge or understanding, and the research questions or hypotheses to be addressed. It also highlights the importance of the research topic and its potential contributions to the field. A well-written background of the study sets the stage for the research and helps the reader to appreciate the need for the study and its potential significance.

How to Write Background of The Study

Here are some steps to help you write the background of the study:

Identify the Research Problem

Start by identifying the research problem you are trying to address. This problem should be significant and relevant to your field of study.

Provide Context

Once you have identified the research problem, provide some context. This could include the historical, social, or political context of the problem.

Review Literature

Conduct a thorough review of the existing literature on the topic. This will help you understand what has been studied and what gaps exist in the current research.

Identify Research Gap

Based on your literature review, identify the gap in knowledge or understanding that your research aims to address. This gap will be the focus of your research question or hypothesis.

State Objectives

Clearly state the objectives of your research . These should be specific, measurable, achievable, relevant, and time-bound (SMART).

Discuss Significance

Explain the significance of your research. This could include its potential impact on theory , practice, policy, or society.

Finally, summarize the key points of the background of the study. This will help the reader understand the research problem, its context, and its significance.

How to Write Background of The Study in Proposal

The background of the study is an essential part of any proposal as it sets the stage for the research project and provides the context and justification for why the research is needed. Here are the steps to write a compelling background of the study in your proposal:

• Identify the problem: Clearly state the research problem or gap in the current knowledge that you intend to address through your research.
• Provide context: Provide a brief overview of the research area and highlight its significance in the field.
• Review literature: Summarize the relevant literature related to the research problem and provide a critical evaluation of the current state of knowledge.
• Identify gaps : Identify the gaps or limitations in the existing literature and explain how your research will contribute to filling these gaps.
• Justify the study : Explain why your research is important and what practical or theoretical contributions it can make to the field.
• Highlight objectives: Clearly state the objectives of the study and how they relate to the research problem.
• Discuss methodology: Provide an overview of the methodology you will use to collect and analyze data, and explain why it is appropriate for the research problem.
• Conclude : Summarize the key points of the background of the study and explain how they support your research proposal.

How to Write Background of The Study In Thesis

The background of the study is a critical component of a thesis as it provides context for the research problem, rationale for conducting the study, and the significance of the research. Here are some steps to help you write a strong background of the study:

• Identify the research problem : Start by identifying the research problem that your thesis is addressing. What is the issue that you are trying to solve or explore? Be specific and concise in your problem statement.
• Review the literature: Conduct a thorough review of the relevant literature on the topic. This should include scholarly articles, books, and other sources that are directly related to your research question.
• I dentify gaps in the literature: After reviewing the literature, identify any gaps in the existing research. What questions remain unanswered? What areas have not been explored? This will help you to establish the need for your research.
• Establish the significance of the research: Clearly state the significance of your research. Why is it important to address this research problem? What are the potential implications of your research? How will it contribute to the field?
• Provide an overview of the research design: Provide an overview of the research design and methodology that you will be using in your study. This should include a brief explanation of the research approach, data collection methods, and data analysis techniques.
• State the research objectives and research questions: Clearly state the research objectives and research questions that your study aims to answer. These should be specific, measurable, achievable, relevant, and time-bound.
• Summarize the chapter: Summarize the chapter by highlighting the key points and linking them back to the research problem, significance of the study, and research questions.

How to Write Background of The Study in Research Paper

Here are the steps to write the background of the study in a research paper:

• Identify the research problem: Start by identifying the research problem that your study aims to address. This can be a particular issue, a gap in the literature, or a need for further investigation.
• Conduct a literature review: Conduct a thorough literature review to gather information on the topic, identify existing studies, and understand the current state of research. This will help you identify the gap in the literature that your study aims to fill.
• Explain the significance of the study: Explain why your study is important and why it is necessary. This can include the potential impact on the field, the importance to society, or the need to address a particular issue.
• Provide context: Provide context for the research problem by discussing the broader social, economic, or political context that the study is situated in. This can help the reader understand the relevance of the study and its potential implications.
• State the research questions and objectives: State the research questions and objectives that your study aims to address. This will help the reader understand the scope of the study and its purpose.
• Summarize the methodology : Briefly summarize the methodology you used to conduct the study, including the data collection and analysis methods. This can help the reader understand how the study was conducted and its reliability.

Examples of Background of The Study

Here are some examples of the background of the study:

Problem : The prevalence of obesity among children in the United States has reached alarming levels, with nearly one in five children classified as obese.

Significance : Obesity in childhood is associated with numerous negative health outcomes, including increased risk of type 2 diabetes, cardiovascular disease, and certain cancers.

Gap in knowledge : Despite efforts to address the obesity epidemic, rates continue to rise. There is a need for effective interventions that target the unique needs of children and their families.

Problem : The use of antibiotics in agriculture has contributed to the development of antibiotic-resistant bacteria, which poses a significant threat to human health.

Significance : Antibiotic-resistant infections are responsible for thousands of deaths each year and are a major public health concern.

Gap in knowledge: While there is a growing body of research on the use of antibiotics in agriculture, there is still much to be learned about the mechanisms of resistance and the most effective strategies for reducing antibiotic use.

Edxample 3:

Problem : Many low-income communities lack access to healthy food options, leading to high rates of food insecurity and diet-related diseases.

Significance : Poor nutrition is a major contributor to chronic diseases such as obesity, type 2 diabetes, and cardiovascular disease.

Gap in knowledge : While there have been efforts to address food insecurity, there is a need for more research on the barriers to accessing healthy food in low-income communities and effective strategies for increasing access.

Examples of Background of The Study In Research

Here are some real-life examples of how the background of the study can be written in different fields of study:

Example 1 : “There has been a significant increase in the incidence of diabetes in recent years. This has led to an increased demand for effective diabetes management strategies. The purpose of this study is to evaluate the effectiveness of a new diabetes management program in improving patient outcomes.”

Example 2 : “The use of social media has become increasingly prevalent in modern society. Despite its popularity, little is known about the effects of social media use on mental health. This study aims to investigate the relationship between social media use and mental health in young adults.”

Example 3: “Despite significant advancements in cancer treatment, the survival rate for patients with pancreatic cancer remains low. The purpose of this study is to identify potential biomarkers that can be used to improve early detection and treatment of pancreatic cancer.”

Examples of Background of The Study in Proposal

Here are some real-time examples of the background of the study in a proposal:

Example 1 : The prevalence of mental health issues among university students has been increasing over the past decade. This study aims to investigate the causes and impacts of mental health issues on academic performance and wellbeing.

Example 2 : Climate change is a global issue that has significant implications for agriculture in developing countries. This study aims to examine the adaptive capacity of smallholder farmers to climate change and identify effective strategies to enhance their resilience.

Example 3 : The use of social media in political campaigns has become increasingly common in recent years. This study aims to analyze the effectiveness of social media campaigns in mobilizing young voters and influencing their voting behavior.

Example 4 : Employee turnover is a major challenge for organizations, especially in the service sector. This study aims to identify the key factors that influence employee turnover in the hospitality industry and explore effective strategies for reducing turnover rates.

Examples of Background of The Study in Thesis

Here are some real-time examples of the background of the study in the thesis:

Example 1 : “Women’s participation in the workforce has increased significantly over the past few decades. However, women continue to be underrepresented in leadership positions, particularly in male-dominated industries such as technology. This study aims to examine the factors that contribute to the underrepresentation of women in leadership roles in the technology industry, with a focus on organizational culture and gender bias.”

Example 2 : “Mental health is a critical component of overall health and well-being. Despite increased awareness of the importance of mental health, there are still significant gaps in access to mental health services, particularly in low-income and rural communities. This study aims to evaluate the effectiveness of a community-based mental health intervention in improving mental health outcomes in underserved populations.”

Example 3: “The use of technology in education has become increasingly widespread, with many schools adopting online learning platforms and digital resources. However, there is limited research on the impact of technology on student learning outcomes and engagement. This study aims to explore the relationship between technology use and academic achievement among middle school students, as well as the factors that mediate this relationship.”

Examples of Background of The Study in Research Paper

Here are some examples of how the background of the study can be written in various fields:

Example 1: The prevalence of obesity has been on the rise globally, with the World Health Organization reporting that approximately 650 million adults were obese in 2016. Obesity is a major risk factor for several chronic diseases such as diabetes, cardiovascular diseases, and cancer. In recent years, several interventions have been proposed to address this issue, including lifestyle changes, pharmacotherapy, and bariatric surgery. However, there is a lack of consensus on the most effective intervention for obesity management. This study aims to investigate the efficacy of different interventions for obesity management and identify the most effective one.

Example 2: Antibiotic resistance has become a major public health threat worldwide. Infections caused by antibiotic-resistant bacteria are associated with longer hospital stays, higher healthcare costs, and increased mortality. The inappropriate use of antibiotics is one of the main factors contributing to the development of antibiotic resistance. Despite numerous efforts to promote the rational use of antibiotics, studies have shown that many healthcare providers continue to prescribe antibiotics inappropriately. This study aims to explore the factors influencing healthcare providers’ prescribing behavior and identify strategies to improve antibiotic prescribing practices.

Example 3: Social media has become an integral part of modern communication, with millions of people worldwide using platforms such as Facebook, Twitter, and Instagram. Social media has several advantages, including facilitating communication, connecting people, and disseminating information. However, social media use has also been associated with several negative outcomes, including cyberbullying, addiction, and mental health problems. This study aims to investigate the impact of social media use on mental health and identify the factors that mediate this relationship.

Purpose of Background of The Study

The primary purpose of the background of the study is to help the reader understand the rationale for the research by presenting the historical, theoretical, and empirical background of the problem.

More specifically, the background of the study aims to:

• Provide a clear understanding of the research problem and its context.
• Identify the gap in knowledge that the study intends to fill.
• Establish the significance of the research problem and its potential contribution to the field.
• Highlight the key concepts, theories, and research findings related to the problem.
• Provide a rationale for the research questions or hypotheses and the research design.
• Identify the limitations and scope of the study.

When to Write Background of The Study

The background of the study should be written early on in the research process, ideally before the research design is finalized and data collection begins. This allows the researcher to clearly articulate the rationale for the study and establish a strong foundation for the research.

The background of the study typically comes after the introduction but before the literature review section. It should provide an overview of the research problem and its context, and also introduce the key concepts, theories, and research findings related to the problem.

Writing the background of the study early on in the research process also helps to identify potential gaps in knowledge and areas for further investigation, which can guide the development of the research questions or hypotheses and the research design. By establishing the significance of the research problem and its potential contribution to the field, the background of the study can also help to justify the research and secure funding or support from stakeholders.

Advantage of Background of The Study

The background of the study has several advantages, including:

• Provides context: The background of the study provides context for the research problem by highlighting the historical, theoretical, and empirical background of the problem. This allows the reader to understand the research problem in its broader context and appreciate its significance.
• Identifies gaps in knowledge: By reviewing the existing literature related to the research problem, the background of the study can identify gaps in knowledge that the study intends to fill. This helps to establish the novelty and originality of the research and its potential contribution to the field.
• Justifies the research : The background of the study helps to justify the research by demonstrating its significance and potential impact. This can be useful in securing funding or support for the research.
• Guides the research design: The background of the study can guide the development of the research questions or hypotheses and the research design by identifying key concepts, theories, and research findings related to the problem. This ensures that the research is grounded in existing knowledge and is designed to address the research problem effectively.
• Establishes credibility: By demonstrating the researcher’s knowledge of the field and the research problem, the background of the study can establish the researcher’s credibility and expertise, which can enhance the trustworthiness and validity of the research.

Disadvantages of Background of The Study

Some Disadvantages of Background of The Study are as follows:

• Time-consuming : Writing a comprehensive background of the study can be time-consuming, especially if the research problem is complex and multifaceted. This can delay the research process and impact the timeline for completing the study.
• Repetitive: The background of the study can sometimes be repetitive, as it often involves summarizing existing research and theories related to the research problem. This can be tedious for the reader and may make the section less engaging.
• Limitations of existing research: The background of the study can reveal the limitations of existing research related to the problem. This can create challenges for the researcher in developing research questions or hypotheses that address the gaps in knowledge identified in the background of the study.
• Bias : The researcher’s biases and perspectives can influence the content and tone of the background of the study. This can impact the reader’s perception of the research problem and may influence the validity of the research.
• Accessibility: Accessing and reviewing the literature related to the research problem can be challenging, especially if the researcher does not have access to a comprehensive database or if the literature is not available in the researcher’s language. This can limit the depth and scope of the background of the study.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Data Collection – Methods Types and Examples

Delimitations in Research – Types, Examples and...

Research Process – Steps, Examples and Tips

Research Design – Types, Methods and Examples

Institutional Review Board – Application Sample...

Evaluating Research – Process, Examples and...

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• CBE Life Sci Educ
• v.21(3); Fall 2022

Literature Reviews, Theoretical Frameworks, and Conceptual Frameworks: An Introduction for New Biology Education Researchers

Julie a. luft.

† Department of Mathematics, Social Studies, and Science Education, Mary Frances Early College of Education, University of Georgia, Athens, GA 30602-7124

Sophia Jeong

‡ Department of Teaching & Learning, College of Education & Human Ecology, Ohio State University, Columbus, OH 43210

Robert Idsardi

§ Department of Biology, Eastern Washington University, Cheney, WA 99004

Grant Gardner

∥ Department of Biology, Middle Tennessee State University, Murfreesboro, TN 37132

Associated Data

To frame their work, biology education researchers need to consider the role of literature reviews, theoretical frameworks, and conceptual frameworks as critical elements of the research and writing process. However, these elements can be confusing for scholars new to education research. This Research Methods article is designed to provide an overview of each of these elements and delineate the purpose of each in the educational research process. We describe what biology education researchers should consider as they conduct literature reviews, identify theoretical frameworks, and construct conceptual frameworks. Clarifying these different components of educational research studies can be helpful to new biology education researchers and the biology education research community at large in situating their work in the broader scholarly literature.

INTRODUCTION

Discipline-based education research (DBER) involves the purposeful and situated study of teaching and learning in specific disciplinary areas ( Singer et al. , 2012 ). Studies in DBER are guided by research questions that reflect disciplines’ priorities and worldviews. Researchers can use quantitative data, qualitative data, or both to answer these research questions through a variety of methodological traditions. Across all methodologies, there are different methods associated with planning and conducting educational research studies that include the use of surveys, interviews, observations, artifacts, or instruments. Ensuring the coherence of these elements to the discipline’s perspective also involves situating the work in the broader scholarly literature. The tools for doing this include literature reviews, theoretical frameworks, and conceptual frameworks. However, the purpose and function of each of these elements is often confusing to new education researchers. The goal of this article is to introduce new biology education researchers to these three important elements important in DBER scholarship and the broader educational literature.

The first element we discuss is a review of research (literature reviews), which highlights the need for a specific research question, study problem, or topic of investigation. Literature reviews situate the relevance of the study within a topic and a field. The process may seem familiar to science researchers entering DBER fields, but new researchers may still struggle in conducting the review. Booth et al. (2016b) highlight some of the challenges novice education researchers face when conducting a review of literature. They point out that novice researchers struggle in deciding how to focus the review, determining the scope of articles needed in the review, and knowing how to be critical of the articles in the review. Overcoming these challenges (and others) can help novice researchers construct a sound literature review that can inform the design of the study and help ensure the work makes a contribution to the field.

The second and third highlighted elements are theoretical and conceptual frameworks. These guide biology education research (BER) studies, and may be less familiar to science researchers. These elements are important in shaping the construction of new knowledge. Theoretical frameworks offer a way to explain and interpret the studied phenomenon, while conceptual frameworks clarify assumptions about the studied phenomenon. Despite the importance of these constructs in educational research, biology educational researchers have noted the limited use of theoretical or conceptual frameworks in published work ( DeHaan, 2011 ; Dirks, 2011 ; Lo et al. , 2019 ). In reviewing articles published in CBE—Life Sciences Education ( LSE ) between 2015 and 2019, we found that fewer than 25% of the research articles had a theoretical or conceptual framework (see the Supplemental Information), and at times there was an inconsistent use of theoretical and conceptual frameworks. Clearly, these frameworks are challenging for published biology education researchers, which suggests the importance of providing some initial guidance to new biology education researchers.

Fortunately, educational researchers have increased their explicit use of these frameworks over time, and this is influencing educational research in science, technology, engineering, and mathematics (STEM) fields. For instance, a quick search for theoretical or conceptual frameworks in the abstracts of articles in Educational Research Complete (a common database for educational research) in STEM fields demonstrates a dramatic change over the last 20 years: from only 778 articles published between 2000 and 2010 to 5703 articles published between 2010 and 2020, a more than sevenfold increase. Greater recognition of the importance of these frameworks is contributing to DBER authors being more explicit about such frameworks in their studies.

Collectively, literature reviews, theoretical frameworks, and conceptual frameworks work to guide methodological decisions and the elucidation of important findings. Each offers a different perspective on the problem of study and is an essential element in all forms of educational research. As new researchers seek to learn about these elements, they will find different resources, a variety of perspectives, and many suggestions about the construction and use of these elements. The wide range of available information can overwhelm the new researcher who just wants to learn the distinction between these elements or how to craft them adequately.

Our goal in writing this paper is not to offer specific advice about how to write these sections in scholarly work. Instead, we wanted to introduce these elements to those who are new to BER and who are interested in better distinguishing one from the other. In this paper, we share the purpose of each element in BER scholarship, along with important points on its construction. We also provide references for additional resources that may be beneficial to better understanding each element. Table 1 summarizes the key distinctions among these elements.

Comparison of literature reviews, theoretical frameworks, and conceptual reviews

This article is written for the new biology education researcher who is just learning about these different elements or for scientists looking to become more involved in BER. It is a result of our own work as science education and biology education researchers, whether as graduate students and postdoctoral scholars or newly hired and established faculty members. This is the article we wish had been available as we started to learn about these elements or discussed them with new educational researchers in biology.

LITERATURE REVIEWS

Purpose of a literature review.

A literature review is foundational to any research study in education or science. In education, a well-conceptualized and well-executed review provides a summary of the research that has already been done on a specific topic and identifies questions that remain to be answered, thus illustrating the current research project’s potential contribution to the field and the reasoning behind the methodological approach selected for the study ( Maxwell, 2012 ). BER is an evolving disciplinary area that is redefining areas of conceptual emphasis as well as orientations toward teaching and learning (e.g., Labov et al. , 2010 ; American Association for the Advancement of Science, 2011 ; Nehm, 2019 ). As a result, building comprehensive, critical, purposeful, and concise literature reviews can be a challenge for new biology education researchers.

Building Literature Reviews

There are different ways to approach and construct a literature review. Booth et al. (2016a) provide an overview that includes, for example, scoping reviews, which are focused only on notable studies and use a basic method of analysis, and integrative reviews, which are the result of exhaustive literature searches across different genres. Underlying each of these different review processes are attention to the s earch process, a ppraisa l of articles, s ynthesis of the literature, and a nalysis: SALSA ( Booth et al. , 2016a ). This useful acronym can help the researcher focus on the process while building a specific type of review.

However, new educational researchers often have questions about literature reviews that are foundational to SALSA or other approaches. Common questions concern determining which literature pertains to the topic of study or the role of the literature review in the design of the study. This section addresses such questions broadly while providing general guidance for writing a narrative literature review that evaluates the most pertinent studies.

The literature review process should begin before the research is conducted. As Boote and Beile (2005 , p. 3) suggested, researchers should be “scholars before researchers.” They point out that having a good working knowledge of the proposed topic helps illuminate avenues of study. Some subject areas have a deep body of work to read and reflect upon, providing a strong foundation for developing the research question(s). For instance, the teaching and learning of evolution is an area of long-standing interest in the BER community, generating many studies (e.g., Perry et al. , 2008 ; Barnes and Brownell, 2016 ) and reviews of research (e.g., Sickel and Friedrichsen, 2013 ; Ziadie and Andrews, 2018 ). Emerging areas of BER include the affective domain, issues of transfer, and metacognition ( Singer et al. , 2012 ). Many studies in these areas are transdisciplinary and not always specific to biology education (e.g., Rodrigo-Peiris et al. , 2018 ; Kolpikova et al. , 2019 ). These newer areas may require reading outside BER; fortunately, summaries of some of these topics can be found in the Current Insights section of the LSE website.

In focusing on a specific problem within a broader research strand, a new researcher will likely need to examine research outside BER. Depending upon the area of study, the expanded reading list might involve a mix of BER, DBER, and educational research studies. Determining the scope of the reading is not always straightforward. A simple way to focus one’s reading is to create a “summary phrase” or “research nugget,” which is a very brief descriptive statement about the study. It should focus on the essence of the study, for example, “first-year nonmajor students’ understanding of evolution,” “metacognitive prompts to enhance learning during biochemistry,” or “instructors’ inquiry-based instructional practices after professional development programming.” This type of phrase should help a new researcher identify two or more areas to review that pertain to the study. Focusing on recent research in the last 5 years is a good first step. Additional studies can be identified by reading relevant works referenced in those articles. It is also important to read seminal studies that are more than 5 years old. Reading a range of studies should give the researcher the necessary command of the subject in order to suggest a research question.

Given that the research question(s) arise from the literature review, the review should also substantiate the selected methodological approach. The review and research question(s) guide the researcher in determining how to collect and analyze data. Often the methodological approach used in a study is selected to contribute knowledge that expands upon what has been published previously about the topic (see Institute of Education Sciences and National Science Foundation, 2013 ). An emerging topic of study may need an exploratory approach that allows for a description of the phenomenon and development of a potential theory. This could, but not necessarily, require a methodological approach that uses interviews, observations, surveys, or other instruments. An extensively studied topic may call for the additional understanding of specific factors or variables; this type of study would be well suited to a verification or a causal research design. These could entail a methodological approach that uses valid and reliable instruments, observations, or interviews to determine an effect in the studied event. In either of these examples, the researcher(s) may use a qualitative, quantitative, or mixed methods methodological approach.

Even with a good research question, there is still more reading to be done. The complexity and focus of the research question dictates the depth and breadth of the literature to be examined. Questions that connect multiple topics can require broad literature reviews. For instance, a study that explores the impact of a biology faculty learning community on the inquiry instruction of faculty could have the following review areas: learning communities among biology faculty, inquiry instruction among biology faculty, and inquiry instruction among biology faculty as a result of professional learning. Biology education researchers need to consider whether their literature review requires studies from different disciplines within or outside DBER. For the example given, it would be fruitful to look at research focused on learning communities with faculty in STEM fields or in general education fields that result in instructional change. It is important not to be too narrow or too broad when reading. When the conclusions of articles start to sound similar or no new insights are gained, the researcher likely has a good foundation for a literature review. This level of reading should allow the researcher to demonstrate a mastery in understanding the researched topic, explain the suitability of the proposed research approach, and point to the need for the refined research question(s).

The literature review should include the researcher’s evaluation and critique of the selected studies. A researcher may have a large collection of studies, but not all of the studies will follow standards important in the reporting of empirical work in the social sciences. The American Educational Research Association ( Duran et al. , 2006 ), for example, offers a general discussion about standards for such work: an adequate review of research informing the study, the existence of sound and appropriate data collection and analysis methods, and appropriate conclusions that do not overstep or underexplore the analyzed data. The Institute of Education Sciences and National Science Foundation (2013) also offer Common Guidelines for Education Research and Development that can be used to evaluate collected studies.

Because not all journals adhere to such standards, it is important that a researcher review each study to determine the quality of published research, per the guidelines suggested earlier. In some instances, the research may be fatally flawed. Examples of such flaws include data that do not pertain to the question, a lack of discussion about the data collection, poorly constructed instruments, or an inadequate analysis. These types of errors result in studies that are incomplete, error-laden, or inaccurate and should be excluded from the review. Most studies have limitations, and the author(s) often make them explicit. For instance, there may be an instructor effect, recognized bias in the analysis, or issues with the sample population. Limitations are usually addressed by the research team in some way to ensure a sound and acceptable research process. Occasionally, the limitations associated with the study can be significant and not addressed adequately, which leaves a consequential decision in the hands of the researcher. Providing critiques of studies in the literature review process gives the reader confidence that the researcher has carefully examined relevant work in preparation for the study and, ultimately, the manuscript.

A solid literature review clearly anchors the proposed study in the field and connects the research question(s), the methodological approach, and the discussion. Reviewing extant research leads to research questions that will contribute to what is known in the field. By summarizing what is known, the literature review points to what needs to be known, which in turn guides decisions about methodology. Finally, notable findings of the new study are discussed in reference to those described in the literature review.

Within published BER studies, literature reviews can be placed in different locations in an article. When included in the introductory section of the study, the first few paragraphs of the manuscript set the stage, with the literature review following the opening paragraphs. Cooper et al. (2019) illustrate this approach in their study of course-based undergraduate research experiences (CUREs). An introduction discussing the potential of CURES is followed by an analysis of the existing literature relevant to the design of CUREs that allows for novel student discoveries. Within this review, the authors point out contradictory findings among research on novel student discoveries. This clarifies the need for their study, which is described and highlighted through specific research aims.

A literature reviews can also make up a separate section in a paper. For example, the introduction to Todd et al. (2019) illustrates the need for their research topic by highlighting the potential of learning progressions (LPs) and suggesting that LPs may help mitigate learning loss in genetics. At the end of the introduction, the authors state their specific research questions. The review of literature following this opening section comprises two subsections. One focuses on learning loss in general and examines a variety of studies and meta-analyses from the disciplines of medical education, mathematics, and reading. The second section focuses specifically on LPs in genetics and highlights student learning in the midst of LPs. These separate reviews provide insights into the stated research question.

Suggestions and Advice

A well-conceptualized, comprehensive, and critical literature review reveals the understanding of the topic that the researcher brings to the study. Literature reviews should not be so big that there is no clear area of focus; nor should they be so narrow that no real research question arises. The task for a researcher is to craft an efficient literature review that offers a critical analysis of published work, articulates the need for the study, guides the methodological approach to the topic of study, and provides an adequate foundation for the discussion of the findings.

In our own writing of literature reviews, there are often many drafts. An early draft may seem well suited to the study because the need for and approach to the study are well described. However, as the results of the study are analyzed and findings begin to emerge, the existing literature review may be inadequate and need revision. The need for an expanded discussion about the research area can result in the inclusion of new studies that support the explanation of a potential finding. The literature review may also prove to be too broad. Refocusing on a specific area allows for more contemplation of a finding.

It should be noted that there are different types of literature reviews, and many books and articles have been written about the different ways to embark on these types of reviews. Among these different resources, the following may be helpful in considering how to refine the review process for scholarly journals:

• Booth, A., Sutton, A., & Papaioannou, D. (2016a). Systemic approaches to a successful literature review (2nd ed.). Los Angeles, CA: Sage. This book addresses different types of literature reviews and offers important suggestions pertaining to defining the scope of the literature review and assessing extant studies.
• Booth, W. C., Colomb, G. G., Williams, J. M., Bizup, J., & Fitzgerald, W. T. (2016b). The craft of research (4th ed.). Chicago: University of Chicago Press. This book can help the novice consider how to make the case for an area of study. While this book is not specifically about literature reviews, it offers suggestions about making the case for your study.
• Galvan, J. L., & Galvan, M. C. (2017). Writing literature reviews: A guide for students of the social and behavioral sciences (7th ed.). Routledge. This book offers guidance on writing different types of literature reviews. For the novice researcher, there are useful suggestions for creating coherent literature reviews.

THEORETICAL FRAMEWORKS

Purpose of theoretical frameworks.

As new education researchers may be less familiar with theoretical frameworks than with literature reviews, this discussion begins with an analogy. Envision a biologist, chemist, and physicist examining together the dramatic effect of a fog tsunami over the ocean. A biologist gazing at this phenomenon may be concerned with the effect of fog on various species. A chemist may be interested in the chemical composition of the fog as water vapor condenses around bits of salt. A physicist may be focused on the refraction of light to make fog appear to be “sitting” above the ocean. While observing the same “objective event,” the scientists are operating under different theoretical frameworks that provide a particular perspective or “lens” for the interpretation of the phenomenon. Each of these scientists brings specialized knowledge, experiences, and values to this phenomenon, and these influence the interpretation of the phenomenon. The scientists’ theoretical frameworks influence how they design and carry out their studies and interpret their data.

Within an educational study, a theoretical framework helps to explain a phenomenon through a particular lens and challenges and extends existing knowledge within the limitations of that lens. Theoretical frameworks are explicitly stated by an educational researcher in the paper’s framework, theory, or relevant literature section. The framework shapes the types of questions asked, guides the method by which data are collected and analyzed, and informs the discussion of the results of the study. It also reveals the researcher’s subjectivities, for example, values, social experience, and viewpoint ( Allen, 2017 ). It is essential that a novice researcher learn to explicitly state a theoretical framework, because all research questions are being asked from the researcher’s implicit or explicit assumptions of a phenomenon of interest ( Schwandt, 2000 ).

Selecting Theoretical Frameworks

Theoretical frameworks are one of the most contemplated elements in our work in educational research. In this section, we share three important considerations for new scholars selecting a theoretical framework.

The first step in identifying a theoretical framework involves reflecting on the phenomenon within the study and the assumptions aligned with the phenomenon. The phenomenon involves the studied event. There are many possibilities, for example, student learning, instructional approach, or group organization. A researcher holds assumptions about how the phenomenon will be effected, influenced, changed, or portrayed. It is ultimately the researcher’s assumption(s) about the phenomenon that aligns with a theoretical framework. An example can help illustrate how a researcher’s reflection on the phenomenon and acknowledgment of assumptions can result in the identification of a theoretical framework.

In our example, a biology education researcher may be interested in exploring how students’ learning of difficult biological concepts can be supported by the interactions of group members. The phenomenon of interest is the interactions among the peers, and the researcher assumes that more knowledgeable students are important in supporting the learning of the group. As a result, the researcher may draw on Vygotsky’s (1978) sociocultural theory of learning and development that is focused on the phenomenon of student learning in a social setting. This theory posits the critical nature of interactions among students and between students and teachers in the process of building knowledge. A researcher drawing upon this framework holds the assumption that learning is a dynamic social process involving questions and explanations among students in the classroom and that more knowledgeable peers play an important part in the process of building conceptual knowledge.

It is important to state at this point that there are many different theoretical frameworks. Some frameworks focus on learning and knowing, while other theoretical frameworks focus on equity, empowerment, or discourse. Some frameworks are well articulated, and others are still being refined. For a new researcher, it can be challenging to find a theoretical framework. Two of the best ways to look for theoretical frameworks is through published works that highlight different frameworks.

When a theoretical framework is selected, it should clearly connect to all parts of the study. The framework should augment the study by adding a perspective that provides greater insights into the phenomenon. It should clearly align with the studies described in the literature review. For instance, a framework focused on learning would correspond to research that reported different learning outcomes for similar studies. The methods for data collection and analysis should also correspond to the framework. For instance, a study about instructional interventions could use a theoretical framework concerned with learning and could collect data about the effect of the intervention on what is learned. When the data are analyzed, the theoretical framework should provide added meaning to the findings, and the findings should align with the theoretical framework.

A study by Jensen and Lawson (2011) provides an example of how a theoretical framework connects different parts of the study. They compared undergraduate biology students in heterogeneous and homogeneous groups over the course of a semester. Jensen and Lawson (2011) assumed that learning involved collaboration and more knowledgeable peers, which made Vygotsky’s (1978) theory a good fit for their study. They predicted that students in heterogeneous groups would experience greater improvement in their reasoning abilities and science achievements with much of the learning guided by the more knowledgeable peers.

In the enactment of the study, they collected data about the instruction in traditional and inquiry-oriented classes, while the students worked in homogeneous or heterogeneous groups. To determine the effect of working in groups, the authors also measured students’ reasoning abilities and achievement. Each data-collection and analysis decision connected to understanding the influence of collaborative work.

Their findings highlighted aspects of Vygotsky’s (1978) theory of learning. One finding, for instance, posited that inquiry instruction, as a whole, resulted in reasoning and achievement gains. This links to Vygotsky (1978) , because inquiry instruction involves interactions among group members. A more nuanced finding was that group composition had a conditional effect. Heterogeneous groups performed better with more traditional and didactic instruction, regardless of the reasoning ability of the group members. Homogeneous groups worked better during interaction-rich activities for students with low reasoning ability. The authors attributed the variation to the different types of helping behaviors of students. High-performing students provided the answers, while students with low reasoning ability had to work collectively through the material. In terms of Vygotsky (1978) , this finding provided new insights into the learning context in which productive interactions can occur for students.

Another consideration in the selection and use of a theoretical framework pertains to its orientation to the study. This can result in the theoretical framework prioritizing individuals, institutions, and/or policies ( Anfara and Mertz, 2014 ). Frameworks that connect to individuals, for instance, could contribute to understanding their actions, learning, or knowledge. Institutional frameworks, on the other hand, offer insights into how institutions, organizations, or groups can influence individuals or materials. Policy theories provide ways to understand how national or local policies can dictate an emphasis on outcomes or instructional design. These different types of frameworks highlight different aspects in an educational setting, which influences the design of the study and the collection of data. In addition, these different frameworks offer a way to make sense of the data. Aligning the data collection and analysis with the framework ensures that a study is coherent and can contribute to the field.

New understandings emerge when different theoretical frameworks are used. For instance, Ebert-May et al. (2015) prioritized the individual level within conceptual change theory (see Posner et al. , 1982 ). In this theory, an individual’s knowledge changes when it no longer fits the phenomenon. Ebert-May et al. (2015) designed a professional development program challenging biology postdoctoral scholars’ existing conceptions of teaching. The authors reported that the biology postdoctoral scholars’ teaching practices became more student-centered as they were challenged to explain their instructional decision making. According to the theory, the biology postdoctoral scholars’ dissatisfaction in their descriptions of teaching and learning initiated change in their knowledge and instruction. These results reveal how conceptual change theory can explain the learning of participants and guide the design of professional development programming.

The communities of practice (CoP) theoretical framework ( Lave, 1988 ; Wenger, 1998 ) prioritizes the institutional level , suggesting that learning occurs when individuals learn from and contribute to the communities in which they reside. Grounded in the assumption of community learning, the literature on CoP suggests that, as individuals interact regularly with the other members of their group, they learn about the rules, roles, and goals of the community ( Allee, 2000 ). A study conducted by Gehrke and Kezar (2017) used the CoP framework to understand organizational change by examining the involvement of individual faculty engaged in a cross-institutional CoP focused on changing the instructional practice of faculty at each institution. In the CoP, faculty members were involved in enhancing instructional materials within their department, which aligned with an overarching goal of instituting instruction that embraced active learning. Not surprisingly, Gehrke and Kezar (2017) revealed that faculty who perceived the community culture as important in their work cultivated institutional change. Furthermore, they found that institutional change was sustained when key leaders served as mentors and provided support for faculty, and as faculty themselves developed into leaders. This study reveals the complexity of individual roles in a COP in order to support institutional instructional change.

It is important to explicitly state the theoretical framework used in a study, but elucidating a theoretical framework can be challenging for a new educational researcher. The literature review can help to identify an applicable theoretical framework. Focal areas of the review or central terms often connect to assumptions and assertions associated with the framework that pertain to the phenomenon of interest. Another way to identify a theoretical framework is self-reflection by the researcher on personal beliefs and understandings about the nature of knowledge the researcher brings to the study ( Lysaght, 2011 ). In stating one’s beliefs and understandings related to the study (e.g., students construct their knowledge, instructional materials support learning), an orientation becomes evident that will suggest a particular theoretical framework. Theoretical frameworks are not arbitrary , but purposefully selected.

With experience, a researcher may find expanded roles for theoretical frameworks. Researchers may revise an existing framework that has limited explanatory power, or they may decide there is a need to develop a new theoretical framework. These frameworks can emerge from a current study or the need to explain a phenomenon in a new way. Researchers may also find that multiple theoretical frameworks are necessary to frame and explore a problem, as different frameworks can provide different insights into a problem.

Finally, it is important to recognize that choosing “x” theoretical framework does not necessarily mean a researcher chooses “y” methodology and so on, nor is there a clear-cut, linear process in selecting a theoretical framework for one’s study. In part, the nonlinear process of identifying a theoretical framework is what makes understanding and using theoretical frameworks challenging. For the novice scholar, contemplating and understanding theoretical frameworks is essential. Fortunately, there are articles and books that can help:

• Creswell, J. W. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Los Angeles, CA: Sage. This book provides an overview of theoretical frameworks in general educational research.
• Ding, L. (2019). Theoretical perspectives of quantitative physics education research. Physical Review Physics Education Research , 15 (2), 020101-1–020101-13. This paper illustrates how a DBER field can use theoretical frameworks.
• Nehm, R. (2019). Biology education research: Building integrative frameworks for teaching and learning about living systems. Disciplinary and Interdisciplinary Science Education Research , 1 , ar15. https://doi.org/10.1186/s43031-019-0017-6 . This paper articulates the need for studies in BER to explicitly state theoretical frameworks and provides examples of potential studies.
• Patton, M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice . Sage. This book also provides an overview of theoretical frameworks, but for both research and evaluation.

CONCEPTUAL FRAMEWORKS

Purpose of a conceptual framework.

A conceptual framework is a description of the way a researcher understands the factors and/or variables that are involved in the study and their relationships to one another. The purpose of a conceptual framework is to articulate the concepts under study using relevant literature ( Rocco and Plakhotnik, 2009 ) and to clarify the presumed relationships among those concepts ( Rocco and Plakhotnik, 2009 ; Anfara and Mertz, 2014 ). Conceptual frameworks are different from theoretical frameworks in both their breadth and grounding in established findings. Whereas a theoretical framework articulates the lens through which a researcher views the work, the conceptual framework is often more mechanistic and malleable.

Conceptual frameworks are broader, encompassing both established theories (i.e., theoretical frameworks) and the researchers’ own emergent ideas. Emergent ideas, for example, may be rooted in informal and/or unpublished observations from experience. These emergent ideas would not be considered a “theory” if they are not yet tested, supported by systematically collected evidence, and peer reviewed. However, they do still play an important role in the way researchers approach their studies. The conceptual framework allows authors to clearly describe their emergent ideas so that connections among ideas in the study and the significance of the study are apparent to readers.

Constructing Conceptual Frameworks

Including a conceptual framework in a research study is important, but researchers often opt to include either a conceptual or a theoretical framework. Either may be adequate, but both provide greater insight into the research approach. For instance, a research team plans to test a novel component of an existing theory. In their study, they describe the existing theoretical framework that informs their work and then present their own conceptual framework. Within this conceptual framework, specific topics portray emergent ideas that are related to the theory. Describing both frameworks allows readers to better understand the researchers’ assumptions, orientations, and understanding of concepts being investigated. For example, Connolly et al. (2018) included a conceptual framework that described how they applied a theoretical framework of social cognitive career theory (SCCT) to their study on teaching programs for doctoral students. In their conceptual framework, the authors described SCCT, explained how it applied to the investigation, and drew upon results from previous studies to justify the proposed connections between the theory and their emergent ideas.

In some cases, authors may be able to sufficiently describe their conceptualization of the phenomenon under study in an introduction alone, without a separate conceptual framework section. However, incomplete descriptions of how the researchers conceptualize the components of the study may limit the significance of the study by making the research less intelligible to readers. This is especially problematic when studying topics in which researchers use the same terms for different constructs or different terms for similar and overlapping constructs (e.g., inquiry, teacher beliefs, pedagogical content knowledge, or active learning). Authors must describe their conceptualization of a construct if the research is to be understandable and useful.

There are some key areas to consider regarding the inclusion of a conceptual framework in a study. To begin with, it is important to recognize that conceptual frameworks are constructed by the researchers conducting the study ( Rocco and Plakhotnik, 2009 ; Maxwell, 2012 ). This is different from theoretical frameworks that are often taken from established literature. Researchers should bring together ideas from the literature, but they may be influenced by their own experiences as a student and/or instructor, the shared experiences of others, or thought experiments as they construct a description, model, or representation of their understanding of the phenomenon under study. This is an exercise in intellectual organization and clarity that often considers what is learned, known, and experienced. The conceptual framework makes these constructs explicitly visible to readers, who may have different understandings of the phenomenon based on their prior knowledge and experience. There is no single method to go about this intellectual work.

Reeves et al. (2016) is an example of an article that proposed a conceptual framework about graduate teaching assistant professional development evaluation and research. The authors used existing literature to create a novel framework that filled a gap in current research and practice related to the training of graduate teaching assistants. This conceptual framework can guide the systematic collection of data by other researchers because the framework describes the relationships among various factors that influence teaching and learning. The Reeves et al. (2016) conceptual framework may be modified as additional data are collected and analyzed by other researchers. This is not uncommon, as conceptual frameworks can serve as catalysts for concerted research efforts that systematically explore a phenomenon (e.g., Reynolds et al. , 2012 ; Brownell and Kloser, 2015 ).

Sabel et al. (2017) used a conceptual framework in their exploration of how scaffolds, an external factor, interact with internal factors to support student learning. Their conceptual framework integrated principles from two theoretical frameworks, self-regulated learning and metacognition, to illustrate how the research team conceptualized students’ use of scaffolds in their learning ( Figure 1 ). Sabel et al. (2017) created this model using their interpretations of these two frameworks in the context of their teaching.

Conceptual framework from Sabel et al. (2017) .

A conceptual framework should describe the relationship among components of the investigation ( Anfara and Mertz, 2014 ). These relationships should guide the researcher’s methods of approaching the study ( Miles et al. , 2014 ) and inform both the data to be collected and how those data should be analyzed. Explicitly describing the connections among the ideas allows the researcher to justify the importance of the study and the rigor of the research design. Just as importantly, these frameworks help readers understand why certain components of a system were not explored in the study. This is a challenge in education research, which is rooted in complex environments with many variables that are difficult to control.

For example, Sabel et al. (2017) stated: “Scaffolds, such as enhanced answer keys and reflection questions, can help students and instructors bridge the external and internal factors and support learning” (p. 3). They connected the scaffolds in the study to the three dimensions of metacognition and the eventual transformation of existing ideas into new or revised ideas. Their framework provides a rationale for focusing on how students use two different scaffolds, and not on other factors that may influence a student’s success (self-efficacy, use of active learning, exam format, etc.).

In constructing conceptual frameworks, researchers should address needed areas of study and/or contradictions discovered in literature reviews. By attending to these areas, researchers can strengthen their arguments for the importance of a study. For instance, conceptual frameworks can address how the current study will fill gaps in the research, resolve contradictions in existing literature, or suggest a new area of study. While a literature review describes what is known and not known about the phenomenon, the conceptual framework leverages these gaps in describing the current study ( Maxwell, 2012 ). In the example of Sabel et al. (2017) , the authors indicated there was a gap in the literature regarding how scaffolds engage students in metacognition to promote learning in large classes. Their study helps fill that gap by describing how scaffolds can support students in the three dimensions of metacognition: intelligibility, plausibility, and wide applicability. In another example, Lane (2016) integrated research from science identity, the ethic of care, the sense of belonging, and an expertise model of student success to form a conceptual framework that addressed the critiques of other frameworks. In a more recent example, Sbeglia et al. (2021) illustrated how a conceptual framework influences the methodological choices and inferences in studies by educational researchers.

Sometimes researchers draw upon the conceptual frameworks of other researchers. When a researcher’s conceptual framework closely aligns with an existing framework, the discussion may be brief. For example, Ghee et al. (2016) referred to portions of SCCT as their conceptual framework to explain the significance of their work on students’ self-efficacy and career interests. Because the authors’ conceptualization of this phenomenon aligned with a previously described framework, they briefly mentioned the conceptual framework and provided additional citations that provided more detail for the readers.

Within both the BER and the broader DBER communities, conceptual frameworks have been used to describe different constructs. For example, some researchers have used the term “conceptual framework” to describe students’ conceptual understandings of a biological phenomenon. This is distinct from a researcher’s conceptual framework of the educational phenomenon under investigation, which may also need to be explicitly described in the article. Other studies have presented a research logic model or flowchart of the research design as a conceptual framework. These constructions can be quite valuable in helping readers understand the data-collection and analysis process. However, a model depicting the study design does not serve the same role as a conceptual framework. Researchers need to avoid conflating these constructs by differentiating the researchers’ conceptual framework that guides the study from the research design, when applicable.

Explicitly describing conceptual frameworks is essential in depicting the focus of the study. We have found that being explicit in a conceptual framework means using accepted terminology, referencing prior work, and clearly noting connections between terms. This description can also highlight gaps in the literature or suggest potential contributions to the field of study. A well-elucidated conceptual framework can suggest additional studies that may be warranted. This can also spur other researchers to consider how they would approach the examination of a phenomenon and could result in a revised conceptual framework.

It can be challenging to create conceptual frameworks, but they are important. Below are two resources that could be helpful in constructing and presenting conceptual frameworks in educational research:

• Maxwell, J. A. (2012). Qualitative research design: An interactive approach (3rd ed.). Los Angeles, CA: Sage. Chapter 3 in this book describes how to construct conceptual frameworks.
• Ravitch, S. M., & Riggan, M. (2016). Reason & rigor: How conceptual frameworks guide research . Los Angeles, CA: Sage. This book explains how conceptual frameworks guide the research questions, data collection, data analyses, and interpretation of results.

CONCLUDING THOUGHTS

Literature reviews, theoretical frameworks, and conceptual frameworks are all important in DBER and BER. Robust literature reviews reinforce the importance of a study. Theoretical frameworks connect the study to the base of knowledge in educational theory and specify the researcher’s assumptions. Conceptual frameworks allow researchers to explicitly describe their conceptualization of the relationships among the components of the phenomenon under study. Table 1 provides a general overview of these components in order to assist biology education researchers in thinking about these elements.

It is important to emphasize that these different elements are intertwined. When these elements are aligned and complement one another, the study is coherent, and the study findings contribute to knowledge in the field. When literature reviews, theoretical frameworks, and conceptual frameworks are disconnected from one another, the study suffers. The point of the study is lost, suggested findings are unsupported, or important conclusions are invisible to the researcher. In addition, this misalignment may be costly in terms of time and money.

Conducting a literature review, selecting a theoretical framework, and building a conceptual framework are some of the most difficult elements of a research study. It takes time to understand the relevant research, identify a theoretical framework that provides important insights into the study, and formulate a conceptual framework that organizes the finding. In the research process, there is often a constant back and forth among these elements as the study evolves. With an ongoing refinement of the review of literature, clarification of the theoretical framework, and articulation of a conceptual framework, a sound study can emerge that makes a contribution to the field. This is the goal of BER and education research.

Supplementary Material

• Allee, V. (2000). Knowledge networks and communities of learning . OD Practitioner , 32 ( 4 ), 4–13. [ Google Scholar ]
• Allen, M. (2017). The Sage encyclopedia of communication research methods (Vols. 1–4 ). Los Angeles, CA: Sage. 10.4135/9781483381411 [ CrossRef ] [ Google Scholar ]
• American Association for the Advancement of Science. (2011). Vision and change in undergraduate biology education: A call to action . Washington, DC. [ Google Scholar ]
• Anfara, V. A., Mertz, N. T. (2014). Setting the stage . In Anfara, V. A., Mertz, N. T. (eds.), Theoretical frameworks in qualitative research (pp. 1–22). Sage. [ Google Scholar ]
• Barnes, M. E., Brownell, S. E. (2016). Practices and perspectives of college instructors on addressing religious beliefs when teaching evolution . CBE—Life Sciences Education , 15 ( 2 ), ar18. https://doi.org/10.1187/cbe.15-11-0243 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Boote, D. N., Beile, P. (2005). Scholars before researchers: On the centrality of the dissertation literature review in research preparation . Educational Researcher , 34 ( 6 ), 3–15. 10.3102/0013189x034006003 [ CrossRef ] [ Google Scholar ]
• Booth, A., Sutton, A., Papaioannou, D. (2016a). Systemic approaches to a successful literature review (2nd ed.). Los Angeles, CA: Sage. [ Google Scholar ]
• Booth, W. C., Colomb, G. G., Williams, J. M., Bizup, J., Fitzgerald, W. T. (2016b). The craft of research (4th ed.). Chicago, IL: University of Chicago Press. [ Google Scholar ]
• Brownell, S. E., Kloser, M. J. (2015). Toward a conceptual framework for measuring the effectiveness of course-based undergraduate research experiences in undergraduate biology . Studies in Higher Education , 40 ( 3 ), 525–544. https://doi.org/10.1080/03075079.2015.1004234 [ Google Scholar ]
• Connolly, M. R., Lee, Y. G., Savoy, J. N. (2018). The effects of doctoral teaching development on early-career STEM scholars’ college teaching self-efficacy . CBE—Life Sciences Education , 17 ( 1 ), ar14. https://doi.org/10.1187/cbe.17-02-0039 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Cooper, K. M., Blattman, J. N., Hendrix, T., Brownell, S. E. (2019). The impact of broadly relevant novel discoveries on student project ownership in a traditional lab course turned CURE . CBE—Life Sciences Education , 18 ( 4 ), ar57. https://doi.org/10.1187/cbe.19-06-0113 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Creswell, J. W. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Los Angeles, CA: Sage. [ Google Scholar ]
• DeHaan, R. L. (2011). Education research in the biological sciences: A nine decade review (Paper commissioned by the NAS/NRC Committee on the Status, Contributions, and Future Directions of Discipline Based Education Research) . Washington, DC: National Academies Press. Retrieved May 20, 2022, from www7.nationalacademies.org/bose/DBER_Mee ting2_commissioned_papers_page.html [ Google Scholar ]
• Ding, L. (2019). Theoretical perspectives of quantitative physics education research . Physical Review Physics Education Research , 15 ( 2 ), 020101. [ Google Scholar ]
• Dirks, C. (2011). The current status and future direction of biology education research . Paper presented at: Second Committee Meeting on the Status, Contributions, and Future Directions of Discipline-Based Education Research, 18–19 October (Washington, DC). Retrieved May 20, 2022, from http://sites.nationalacademies.org/DBASSE/BOSE/DBASSE_071087 [ Google Scholar ]
• Duran, R. P., Eisenhart, M. A., Erickson, F. D., Grant, C. A., Green, J. L., Hedges, L. V., Schneider, B. L. (2006). Standards for reporting on empirical social science research in AERA publications: American Educational Research Association . Educational Researcher , 35 ( 6 ), 33–40. [ Google Scholar ]
• Ebert-May, D., Derting, T. L., Henkel, T. P., Middlemis Maher, J., Momsen, J. L., Arnold, B., Passmore, H. A. (2015). Breaking the cycle: Future faculty begin teaching with learner-centered strategies after professional development . CBE—Life Sciences Education , 14 ( 2 ), ar22. https://doi.org/10.1187/cbe.14-12-0222 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Galvan, J. L., Galvan, M. C. (2017). Writing literature reviews: A guide for students of the social and behavioral sciences (7th ed.). New York, NY: Routledge. https://doi.org/10.4324/9781315229386 [ Google Scholar ]
• Gehrke, S., Kezar, A. (2017). The roles of STEM faculty communities of practice in institutional and departmental reform in higher education . American Educational Research Journal , 54 ( 5 ), 803–833. https://doi.org/10.3102/0002831217706736 [ Google Scholar ]
• Ghee, M., Keels, M., Collins, D., Neal-Spence, C., Baker, E. (2016). Fine-tuning summer research programs to promote underrepresented students’ persistence in the STEM pathway . CBE—Life Sciences Education , 15 ( 3 ), ar28. https://doi.org/10.1187/cbe.16-01-0046 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Institute of Education Sciences & National Science Foundation. (2013). Common guidelines for education research and development . Retrieved May 20, 2022, from www.nsf.gov/pubs/2013/nsf13126/nsf13126.pdf
• Jensen, J. L., Lawson, A. (2011). Effects of collaborative group composition and inquiry instruction on reasoning gains and achievement in undergraduate biology . CBE—Life Sciences Education , 10 ( 1 ), 64–73. https://doi.org/10.1187/cbe.19-05-0098 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Kolpikova, E. P., Chen, D. C., Doherty, J. H. (2019). Does the format of preclass reading quizzes matter? An evaluation of traditional and gamified, adaptive preclass reading quizzes . CBE—Life Sciences Education , 18 ( 4 ), ar52. https://doi.org/10.1187/cbe.19-05-0098 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Labov, J. B., Reid, A. H., Yamamoto, K. R. (2010). Integrated biology and undergraduate science education: A new biology education for the twenty-first century? CBE—Life Sciences Education , 9 ( 1 ), 10–16. https://doi.org/10.1187/cbe.09-12-0092 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Lane, T. B. (2016). Beyond academic and social integration: Understanding the impact of a STEM enrichment program on the retention and degree attainment of underrepresented students . CBE—Life Sciences Education , 15 ( 3 ), ar39. https://doi.org/10.1187/cbe.16-01-0070 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Lave, J. (1988). Cognition in practice: Mind, mathematics and culture in everyday life . New York, NY: Cambridge University Press. [ Google Scholar ]
• Lo, S. M., Gardner, G. E., Reid, J., Napoleon-Fanis, V., Carroll, P., Smith, E., Sato, B. K. (2019). Prevailing questions and methodologies in biology education research: A longitudinal analysis of research in CBE — Life Sciences Education and at the Society for the Advancement of Biology Education Research . CBE—Life Sciences Education , 18 ( 1 ), ar9. https://doi.org/10.1187/cbe.18-08-0164 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Lysaght, Z. (2011). Epistemological and paradigmatic ecumenism in “Pasteur’s quadrant:” Tales from doctoral research . In Official Conference Proceedings of the Third Asian Conference on Education in Osaka, Japan . Retrieved May 20, 2022, from http://iafor.org/ace2011_offprint/ACE2011_offprint_0254.pdf
• Maxwell, J. A. (2012). Qualitative research design: An interactive approach (3rd ed.). Los Angeles, CA: Sage. [ Google Scholar ]
• Miles, M. B., Huberman, A. M., Saldaña, J. (2014). Qualitative data analysis (3rd ed.). Los Angeles, CA: Sage. [ Google Scholar ]
• Nehm, R. (2019). Biology education research: Building integrative frameworks for teaching and learning about living systems . Disciplinary and Interdisciplinary Science Education Research , 1 , ar15. https://doi.org/10.1186/s43031-019-0017-6 [ Google Scholar ]
• Patton, M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice . Los Angeles, CA: Sage. [ Google Scholar ]
• Perry, J., Meir, E., Herron, J. C., Maruca, S., Stal, D. (2008). Evaluating two approaches to helping college students understand evolutionary trees through diagramming tasks . CBE—Life Sciences Education , 7 ( 2 ), 193–201. https://doi.org/10.1187/cbe.07-01-0007 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Posner, G. J., Strike, K. A., Hewson, P. W., Gertzog, W. A. (1982). Accommodation of a scientific conception: Toward a theory of conceptual change . Science Education , 66 ( 2 ), 211–227. [ Google Scholar ]
• Ravitch, S. M., Riggan, M. (2016). Reason & rigor: How conceptual frameworks guide research . Los Angeles, CA: Sage. [ Google Scholar ]
• Reeves, T. D., Marbach-Ad, G., Miller, K. R., Ridgway, J., Gardner, G. E., Schussler, E. E., Wischusen, E. W. (2016). A conceptual framework for graduate teaching assistant professional development evaluation and research . CBE—Life Sciences Education , 15 ( 2 ), es2. https://doi.org/10.1187/cbe.15-10-0225 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Reynolds, J. A., Thaiss, C., Katkin, W., Thompson, R. J. Jr. (2012). Writing-to-learn in undergraduate science education: A community-based, conceptually driven approach . CBE—Life Sciences Education , 11 ( 1 ), 17–25. https://doi.org/10.1187/cbe.11-08-0064 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Rocco, T. S., Plakhotnik, M. S. (2009). Literature reviews, conceptual frameworks, and theoretical frameworks: Terms, functions, and distinctions . Human Resource Development Review , 8 ( 1 ), 120–130. https://doi.org/10.1177/1534484309332617 [ Google Scholar ]
• Rodrigo-Peiris, T., Xiang, L., Cassone, V. M. (2018). A low-intensity, hybrid design between a “traditional” and a “course-based” research experience yields positive outcomes for science undergraduate freshmen and shows potential for large-scale application . CBE—Life Sciences Education , 17 ( 4 ), ar53. https://doi.org/10.1187/cbe.17-11-0248 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Sabel, J. L., Dauer, J. T., Forbes, C. T. (2017). Introductory biology students’ use of enhanced answer keys and reflection questions to engage in metacognition and enhance understanding . CBE—Life Sciences Education , 16 ( 3 ), ar40. https://doi.org/10.1187/cbe.16-10-0298 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Sbeglia, G. C., Goodridge, J. A., Gordon, L. H., Nehm, R. H. (2021). Are faculty changing? How reform frameworks, sampling intensities, and instrument measures impact inferences about student-centered teaching practices . CBE—Life Sciences Education , 20 ( 3 ), ar39. https://doi.org/10.1187/cbe.20-11-0259 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Schwandt, T. A. (2000). Three epistemological stances for qualitative inquiry: Interpretivism, hermeneutics, and social constructionism . In Denzin, N. K., Lincoln, Y. S. (Eds.), Handbook of qualitative research (2nd ed., pp. 189–213). Los Angeles, CA: Sage. [ Google Scholar ]
• Sickel, A. J., Friedrichsen, P. (2013). Examining the evolution education literature with a focus on teachers: Major findings, goals for teacher preparation, and directions for future research . Evolution: Education and Outreach , 6 ( 1 ), 23. https://doi.org/10.1186/1936-6434-6-23 [ Google Scholar ]
• Singer, S. R., Nielsen, N. R., Schweingruber, H. A. (2012). Discipline-based education research: Understanding and improving learning in undergraduate science and engineering . Washington, DC: National Academies Press. [ Google Scholar ]
• Todd, A., Romine, W. L., Correa-Menendez, J. (2019). Modeling the transition from a phenotypic to genotypic conceptualization of genetics in a university-level introductory biology context . Research in Science Education , 49 ( 2 ), 569–589. https://doi.org/10.1007/s11165-017-9626-2 [ Google Scholar ]
• Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes . Cambridge, MA: Harvard University Press. [ Google Scholar ]
• Wenger, E. (1998). Communities of practice: Learning as a social system . Systems Thinker , 9 ( 5 ), 2–3. [ Google Scholar ]
• Ziadie, M. A., Andrews, T. C. (2018). Moving evolution education forward: A systematic analysis of literature to identify gaps in collective knowledge for teaching . CBE—Life Sciences Education , 17 ( 1 ), ar11. https://doi.org/10.1187/cbe.17-08-0190 [ PMC free article ] [ PubMed ] [ Google Scholar ]

• Manuscript Preparation

What is the Background of a Study and How Should it be Written?

• 3 minute read

Table of Contents

The background of a study is one of the most important components of a research paper. The quality of the background determines whether the reader will be interested in the rest of the study. Thus, to ensure that the audience is invested in reading the entire research paper, it is important to write an appealing and effective background. So, what constitutes the background of a study, and how must it be written?

What is the background of a study?

The background of a study is the first section of the paper and establishes the context underlying the research. It contains the rationale, the key problem statement, and a brief overview of research questions that are addressed in the rest of the paper. The background forms the crux of the study because it introduces an unaware audience to the research and its importance in a clear and logical manner. At times, the background may even explore whether the study builds on or refutes findings from previous studies. Any relevant information that the readers need to know before delving into the paper should be made available to them in the background.

How is a background different from the introduction?

The introduction of your research paper is presented before the background. Let’s find out what factors differentiate the background from the introduction.

• The introduction only contains preliminary data about the research topic and does not state the purpose of the study. On the contrary, the background clarifies the importance of the study in detail.
• The introduction provides an overview of the research topic from a broader perspective, while the background provides a detailed understanding of the topic.
• The introduction should end with the mention of the research questions, aims, and objectives of the study. In contrast, the background follows no such format and only provides essential context to the study.

How should one write the background of a research paper?

The length and detail presented in the background varies for different research papers, depending on the complexity and novelty of the research topic. At times, a simple background suffices, even if the study is complex. Before writing and adding details in the background, take a note of these additional points:

• Start with a strong beginning: Begin the background by defining the research topic and then identify the target audience.
• Cover key components: Explain all theories, concepts, terms, and ideas that may feel unfamiliar to the target audience thoroughly.
• Take note of important prerequisites: Go through the relevant literature in detail. Take notes while reading and cite the sources.
• Maintain a balance: Make sure that the background is focused on important details, but also appeals to a broader audience.
• Include historical data: Current issues largely originate from historical events or findings. If the research borrows information from a historical context, add relevant data in the background.
• Explain novelty: If the research study or methodology is unique or novel, provide an explanation that helps to understand the research better.
• Increase engagement: To make the background engaging, build a story around the central theme of the research

Avoid these mistakes while writing the background:

• Ambiguity: Don’t be ambiguous. While writing, assume that the reader does not understand any intricate detail about your research.
• Unrelated themes: Steer clear from topics that are not related to the key aspects of your research topic.
• Poor organization: Do not place information without a structure. Make sure that the background reads in a chronological manner and organize the sub-sections so that it flows well.

Writing the background for a research paper should not be a daunting task. But directions to go about it can always help. At Elsevier Author Services we provide essential insights on how to write a high quality, appealing, and logically structured paper for publication, beginning with a robust background. For further queries, contact our experts now!

How to Use Tables and Figures effectively in Research Papers

• Research Process

The Top 5 Qualities of Every Good Researcher

You may also like.

Page-Turner Articles are More Than Just Good Arguments: Be Mindful of Tone and Structure!

A Must-see for Researchers! How to Ensure Inclusivity in Your Scientific Writing

Make Hook, Line, and Sinker: The Art of Crafting Engaging Introductions

Can Describing Study Limitations Improve the Quality of Your Paper?

A Guide to Crafting Shorter, Impactful Sentences in Academic Writing

6 Steps to Write an Excellent Discussion in Your Manuscript

How to Write Clear and Crisp Civil Engineering Papers? Here are 5 Key Tips to Consider

The Clear Path to An Impactful Paper: ②

Input your search keywords and press Enter.

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• Background Information
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

Background information identifies and describes the history and nature of a well-defined research problem with reference to contextualizing existing literature. The background information should indicate the root of the problem being studied, appropriate context of the problem in relation to theory, research, and/or practice , its scope, and the extent to which previous studies have successfully investigated the problem, noting, in particular, where gaps exist that your study attempts to address. Background information does not replace the literature review section of a research paper; it is intended to place the research problem within a specific context and an established plan for its solution.

Fitterling, Lori. Researching and Writing an Effective Background Section of a Research Paper. Kansas City University of Medicine & Biosciences; Creating a Research Paper: How to Write the Background to a Study. DurousseauElectricalInstitute.com; Background Information: Definition of Background Information. Literary Devices Definition and Examples of Literary Terms.

Importance of Having Enough Background Information

Background information expands upon the key points stated in the beginning of your introduction but is not intended to be the main focus of the paper. It generally supports the question, what is the most important information the reader needs to understand before continuing to read the paper? Sufficient background information helps the reader determine if you have a basic understanding of the research problem being investigated and promotes confidence in the overall quality of your analysis and findings. This information provides the reader with the essential context needed to conceptualize the research problem and its significance before moving on to a more thorough analysis of prior research.

Forms of contextualization included in background information can include describing one or more of the following:

• Cultural -- placed within the learned behavior of a specific group or groups of people.
• Economic -- of or relating to systems of production and management of material wealth and/or business activities.
• Gender -- located within the behavioral, cultural, or psychological traits typically associated with being self-identified as male, female, or other form of  gender expression.
• Historical -- the time in which something takes place or was created and how the condition of time influences how you interpret it.
• Interdisciplinary -- explanation of theories, concepts, ideas, or methodologies borrowed from other disciplines applied to the research problem rooted in a discipline other than the discipline where your paper resides.
• Philosophical -- clarification of the essential nature of being or of phenomena as it relates to the research problem.
• Physical/Spatial -- reflects the meaning of space around something and how that influences how it is understood.
• Political -- concerns the environment in which something is produced indicating it's public purpose or agenda.
• Social -- the environment of people that surrounds something's creation or intended audience, reflecting how the people associated with something use and interpret it.
• Temporal -- reflects issues or events of, relating to, or limited by time. Concerns past, present, or future contextualization and not just a historical past.

Background information can also include summaries of important research studies . This can be a particularly important element of providing background information if an innovative or groundbreaking study about the research problem laid a foundation for further research or there was a key study that is essential to understanding your arguments. The priority is to summarize for the reader what is known about the research problem before you conduct the analysis of prior research. This is accomplished with a general summary of the foundational research literature [with citations] that document findings that inform your study's overall aims and objectives.

NOTE: Research studies cited as part of the background information of your introduction should not include very specific, lengthy explanations. This should be discussed in greater detail in your literature review section. If you find a study requiring lengthy explanation, consider moving it to the literature review section.

ANOTHER NOTE: In some cases, your paper's introduction only needs to introduce the research problem, explain its significance, and then describe a road map for how you are going to address the problem; the background information basically forms the introduction part of your literature review. That said, while providing background information is not required, including it in the introduction is a way to highlight important contextual information that could otherwise be hidden or overlooked by the reader if placed in the literature review section.

YET ANOTHER NOTE: In some research studies, the background information is described in a separate section after the introduction and before the literature review. This is most often done if the topic is especially complex or requires a lot of context in order to fully grasp the significance of the research problem. Most college-level research papers do not require this unless required by your professor. However, if you find yourself needing to write more than a couple of pages [double-spaced lines] to provide the background information, it can be written as a separate section to ensure the introduction is not too lengthy.

Background of the Problem Section: What do you Need to Consider? Anonymous. Harvard University; Hopkins, Will G. How to Write a Research Paper. SPORTSCIENCE, Perspectives/Research Resources. Department of Physiology and School of Physical Education, University of Otago, 1999; Green, L. H. How to Write the Background/Introduction Section. Physics 499 Powerpoint slides. University of Illinois; Pyrczak, Fred. Writing Empirical Research Reports: A Basic Guide for Students of the Social and Behavioral Sciences . 8th edition. Glendale, CA: Pyrczak Publishing, 2014; Stevens, Kathleen C. “Can We Improve Reading by Teaching Background Information?.” Journal of Reading 25 (January 1982): 326-329; Woodall, W. Gill. Writing the Background and Significance Section. Senior Research Scientist and Professor of Communication. Center on Alcoholism, Substance Abuse, and Addictions. University of New Mexico.

Structure and Writing Style

Providing background information in the introduction of a research paper serves as a bridge that links the reader to the research problem . Precisely how long and in-depth this bridge should be is largely dependent upon how much information you think the reader will need to know in order to fully understand the problem being discussed and to appreciate why the issues you are investigating are important.

From another perspective, the length and detail of background information also depends on the degree to which you need to demonstrate to your professor how much you understand the research problem. Keep this in mind because providing pertinent background information can be an effective way to demonstrate that you have a clear grasp of key issues, debates, and concepts related to your overall study.

The structure and writing style of your background information can vary depending upon the complexity of your research and/or the nature of the assignment. However, in most cases it should be limited to only one to two paragraphs in your introduction.

Given this, here are some questions to consider while writing this part of your introduction :

• Are there concepts, terms, theories, or ideas that may be unfamiliar to the reader and, thus, require additional explanation?
• Are there historical elements that need to be explored in order to provide needed context, to highlight specific people, issues, or events, or to lay a foundation for understanding the emergence of a current issue or event?
• Are there theories, concepts, or ideas borrowed from other disciplines or academic traditions that may be unfamiliar to the reader and therefore require further explanation?
• Is there a key study or small set of studies that set the stage for understanding the topic and frames why it is important to conduct further research on the topic?
• Y our study uses a method of analysis never applied before;
• Your study investigates a very esoteric or complex research problem;
• Your study introduces new or unique variables that need to be taken into account ; or,
• Your study relies upon analyzing unique texts or documents, such as, archival materials or primary documents like diaries or personal letters that do not represent the established body of source literature on the topic?

Almost all introductions to a research problem require some contextualizing, but the scope and breadth of background information varies depending on your assumption about the reader's level of prior knowledge . However, despite this assessment, background information should be brief and succinct and sets the stage for the elaboration of critical points or in-depth discussion of key issues in the literature review section of your paper.

Writing Tip

Background Information vs. the Literature Review

Incorporating background information into the introduction is intended to provide the reader with critical information about the topic being studied, such as, highlighting and expanding upon foundational studies conducted in the past, describing important historical events that inform why and in what ways the research problem exists, defining key components of your study [concepts, people, places, phenomena] and/or placing the research problem within a particular context. Although introductory background information can often blend into the literature review portion of the paper, essential background information should not be considered a substitute for a comprehensive review and synthesis of relevant research literature.

Hart, Cris. Doing a Literature Review: Releasing the Social Science Research Imagination . Thousand Oaks, CA: Sage, 1998; Pyrczak, Fred. Writing Empirical Research Reports: A Basic Guide for Students of the Social and Behavioral Sciences . 8th edition. Glendale, CA: Pyrczak Publishing, 2014.

• << Previous: The C.A.R.S. Model
• Next: The Research Problem/Question >>
• Last Updated: May 30, 2024 9:38 AM
• URL: https://libguides.usc.edu/writingguide

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 01 March 2024

The multifaceted influence of multidisciplinary background on placement and academic progression of faculty

• Wenjing Lyu 1 ,
• Yuanhao Huang 2 &
• Jin Liu 2  

Humanities and Social Sciences Communications volume  11 , Article number:  350 ( 2024 ) Cite this article

728 Accesses

6 Citations

1 Altmetric

Metrics details

• Social policy

This study delves into the implications of faculty’s multidisciplinary educational backgrounds on their academic placement and upward mobility, and underscores the moderating effects of gender and academic inbreeding. Grounded in the theories of knowledge recombination and limited attention, the study finds that having a multidisciplinary background tends to challenge favorable academic placements and upward mobility. However, it also shows that male faculty and those who have graduated from the same institution where they work (academic inbreeding) are better at overcoming these challenges. Additionally, elite universities seem to have a higher regard for multidisciplinary backgrounds. This study provides insights for individuals navigating academic careers and offers valuable information for university leaders and policymakers.

Similar content being viewed by others

Socioeconomic roots of academic faculty

Quantifying hierarchy and dynamics in US faculty hiring and retention

Academic ecosystems must evolve to support a sustainable postdoc workforce

Introduction.

In academia, addressing complex problems and fostering creative competencies often involves conducting scientific investigations from various disciplinary perspectives (Körner, 2010 ). Universities play a vital role in nurturing creative individuals through multidisciplinary education (James Jacob, 2015 ). In the rapidly evolving field of artificial intelligence, exemplified by advancements like ChatGPT (Dwivedi et al., 2023 ; Odugbesan et al., 2023 ), scholars increasingly need the capacity for holistic academic collaboration and a multidisciplinary outlook to effectively navigate this landscape.

The foundational discipline definitions provided by Biglan’s classification scheme serve as a basis for interdisciplinary and multidisciplinary studies (Simpson, 2017 ). Multidisciplinary education based on this scheme is an essential means for integrating knowledge and addressing complex problems (James Jacob, 2015 ). With knowledge recombination and limited attention perspective (Arts and Fleming, 2018 ; Xiao et al., 2022 ), faculty with a multidisciplinary education background exhibit proficiency across diverse disciplinary domains, boasting a rich and varied knowledge reservoir, thereby fostering an environment conducive to innovation. Therefore, multidisciplinary graduates are more likely to secure academic positions than non-multidisciplinary graduates (Morgan et al., 2022 ), although their depth of knowledge and competence in a single discipline may be questioned when applying for academic positions in academic institutions (Haider et al., 2018 ). While breadth of knowledge is undeniably valuable, there exists a concomitant risk of attenuating depth within specific disciplines (Arts and Fleming, 2018 ). Furthermore, elite universities are more inclined than ordinary universities to engage in high-level interdisciplinary research and offer positions, as suggested (Leahey et al., 2019 ; Li and Yin, 2023 ). This could be advantageous for scholars with multidisciplinary backgrounds in terms of placement opportunities. But there is currently no consensus on the influence of a multidisciplinary background on the academic career development of faculty, given the mixed results across the literature and the lack of empirical evidence. Thus, this study employs a Curriculum Vitae (CV) analytical approach to explore the academic career placement of faculty with multidisciplinary backgrounds.

This study makes several significant contributions by conducting a detailed analysis of the multifaceted impact of faculty’s multidisciplinary education backgrounds on their academic careers. Firstly, it focuses on the influence of multidisciplinary education on faculty’s academic placement and progression, extending the application scenario of Biglan’s classification. Secondly, it resolves the dispute between knowledge recombination theory and limited attention theory regarding their contradictory predictions on multidisciplinarity by applying refined empirical evidence, differentiating elite universities with less prestigious universities. Thirdly, it explains the moderating effect of gender and academic inbreeding on faculty with multidisciplinary education backgrounds.

The following portion of this study, named “Theoretical background and literature review”, goes into relevant literature, expounding on the theories that underpin the study’s findings. The “Data and methodology” section that follows describes the dataset, variables, and research methodology used in this analysis. Following that, the “Results” section explains the statistical analysis and their associated findings. A full discussion is proposed based on these findings. In the final section, “Conclusion, limitations, and future research” concludes with a synthesis of its findings. Furthermore, this section highlights the study’s limitations and suggests potential paths for future research.

Theoretical background and literature review

Biglan’s classification of academic disciplines.

Academic disciplines serve as pivotal matrices for the consolidation and dissemination of knowledge, thereby playing an indispensable role in the intellectual development of a society (Zahra and Newey, 2009 ). A classification model aimed at arranging these disciplines into discernible clusters, postulated by Biglan in 1973 , has since been widely acknowledged and adopted as perhaps the most salient tool for delineating disciplines within the realm of higher education (Biglan, 1973 ). Biglan’s categorization elucidates disciplines across three principal axes: firstly, the dichotomy of “hard” versus “soft”; secondly, the juxtaposition of “pure” against “applied”; and thirdly, the distinction between disciplines pivoted towards biological entities—commonly referred to as “life disciplines”—and those that revolve around abstract or non-biological paradigms, termed “nonlife disciplines” (Simpson, 2017 ; Stoecker, 1993 ).

A plethora of empirical investigations have rigorously assessed the veracity and utility of Biglan’s classification, cementing its position as a mainstay in academic discourse. The observed wage disparities across the pure/applied, hard/soft, and life/nonlife spectrums serve as robust testimonials to the validity of Biglan’s categorization (Muffo and Langston, 1981 ). Such a compendium of research has corroborated the model’s malleability and versatility. Pioneering efforts by scholars like Smart and Elton (Smart and Elton, 1982 ) and Stoecker (Stoecker, 1993 ) employed discriminant function analysis to probe into Biglan’s classification, facilitating the incorporation of emergent academic fields. Adrian Simpson (Simpson, 2017 ) elucidated the alignment of Biglan’s framework underscoring the enduring significance of academic disciplines in shaping the educational topography. The widespread use of Biglan’s disciplines classification scheme attests to its importance (Lindblom-Ylanne et al., 2011 ). Extensive researches utilizing Biglan’s classification have been conducted on academic territories, knowledge combinations, students’ academic performance and learning styles and so on (Chan et al., 2022 ; Dwivedi et al., 2023 ; Mcdossi, 2022 ). The research of multidisciplinary and interdisciplinary are conducted widely based on Biglan’s classification. Academics and universities have been well aware of the benefits of them. To handle complex situations and improve individual’s employment competitiveness, students be encouraged to choose and participate in multidisciplinary or interdisciplinary education.

In some research, the terms “multidisciplinary” and “interdisciplinary” are often used interchangeably (Wiggins and Sawyer, 2012 ). A multidisciplinary approach respects and maintains the distinctiveness of each discipline, it just juxtaposes disciplines (Frodeman, 2010 ). Some scholars have referred to multidisciplinarity as a variant of “incipient interdisciplinarity” or even “quasi-interdisciplinarity” which implies an engagement with multiple disciplines sans profound integration (Feng et al., 2023 ). While multidisciplinary education engages in the pedagogical exploration of various disciplines in silos or different study stages, each retaining its integrity without extensive intermingling of knowledge or methods, interdisciplinary education leans on depth achieved through the synthesis of multiple disciplines to attain a holistic comprehension (Holley, 2009 ). Although there is a conceptual discrepancy between the two, in educational practice, so-called “interdisciplinary” curricula or education carried out by universities or institutions are actually a multidisciplinary assemblage of disciplinary courses, including programs of general education and interdisciplinary fields that ask students to take a selection of department-based courses (Frodeman, 2010 ). If someone has attained interdisciplinary education, he or she has received a multidisciplinary education in fact, whether through learning from multiple disciplinary courses or transitioning between disciplinary fields at different stages of their studies. As thus, in this study, the multidisciplinary educational background in higher education could be expressed in two situations: one is participating in courses covering multiple disciplines, and the other is transitioning between disciplinary fields at the undergraduate, graduate, or postgraduate stage.

Based on Biglan’s disciplines classification, multidisciplinary education is considered to acquire powerful knowledge and enhance learning skills (Hudson et al., 2023 ; Marbach-Ad et al., 2019 ), enabling students to achieve higher academic achievement but may feel challenged (O’Donovan, 2019 ). The influence of a multidisciplinary education background on post-graduation outcomes and career placement have also received attention (Tseng et al., 2023 ). In conclusion, According to Biglan’s classification, multidisciplinary education enables individual access more diverse knowledge and improves their ability to deal complex problems or situations. Further, individual who has multidisciplinary education background will perform better in the job market. Biglan’s classification is one of the best-known and most widely used classifications of academic disciplines or fields of study (Paulsen and Wells, 1998 ; Simpson, 2017 ). It has been widely used in many research fields and education (Staupe-Delgado et al., 2022 ). But it is noted that the two dimensions of Biglan’s classification (i.e., life/non-life) are less considered and applied by scholars (Rosman et al., 2020 ). In recent years, with the expanding utilization of Biglan’s classification, current literature has advocated for its comprehensive application to yield more fruitful insights(Lim and Richardson, 2022 ; Zadravec and Kočar, 2023 ). In response to this call within the literature, this study adopts Biglan’s classification as a standard for disciplinary categorization and designs a multidisciplinary education background based on its principles.

Knowledge recombination and the merit of multidisciplinary education

From the lens of the knowledge recombination paradigm, innovation emerges from the fusion of disparate knowledge units, each rooted in foundational scientific or technical paradigms (Xiao et al., 2022 ). Due to the boundaries and closed nature of knowledge in different external domains, and the emergence of key new ideas and information in the field poses challenges to enterprises and institutions (Ehls et al., 2020 ). Openness to external knowledge has gained popularity as a means for firms and institutions to complement and leverage internal knowledge in the pursuit of innovation outcomes (Wang et al., 2020 ). Solving complex problems requires knowledge and information from multiple disciplines, and it is difficult to rely on a single discipline or knowledge domain for solutions (Kurtzberg, 2005 ; Nandan and London, 2013 ; Wang et al., 2020 ). In science, integrating perspectives, theories, information, and tools from two or more disciplines or fields are manifested as multidisciplinarity (Frodeman, 2010 ), which will address complex problems by combining knowledge from different disciplinary fields (Petersen et al., 2021 ; Xiao et al., 2022 ). In addition, multidisciplinary teams or individuals with knowledge in multiple disciplines are regarded as possess creative competencies, enabling for rich combinations of otherwise disconnected pools of ideas, including more radical ideas and solutions adjusted to complex problems (Hero and Lindfors, 2019 ; Kearney and Gebert, 2009 ). Forming multidisciplinary research teams with professionals from diverse disciplinary backgrounds can effectively address scientific and societal problems (Fontana et al., 2022 ; Nagle and Teodoridis, 2020 ).

Thus, engaging in multidisciplinary education to cultivate individuals with knowledge in multiple disciplines becomes an alternative means for recombining knowledge and addressing complex problems, especially in higher education (James Jacob, 2015 ). Diversified researchers have a more pronounced ability to explore new knowledge domains (Nagle and Teodoridis, 2020 ). Those possessing a vast intellectual reservoir, garnered from multiple disciplines, inevitably cultivate a more adaptable cognitive framework. This diverse foundation capacitates individuals to adeptly synthesize multifaceted knowledge, leading to the genesis of novel and inventive outcomes (Arts and Fleming, 2018 ). Individuals who have received multidisciplinary education are considered to have a multidisciplinary education background (Frodeman, 2010 ). Not only for the benefits in multidisciplinary approaches but also with the consideration of promoting students’ employment, interdisciplinary courses and majors are implemented (Costa et al., 2019 ; Huang et al., 2020a , 2020b ). Graduates with a multidisciplinary background are more competitive in the job market and it is certain that the benefits of a multidisciplinary background become more evident over time (Tseng et al., 2023 ). Notably, some academics have begun to focus on interdisciplinary education’s influence on academic career and placement (Holley, 2018 ).

The research of academic career has received scholars’ amount of attention and is becoming mature. The career development of doctoral students and scholars, as well as various factors influencing academic careers such as academic productivity, have been under spotlight (Long et al., 1998 ; Ryazanova and Jaskiene, 2022 ). In particular, the influence of multidisciplinary education on the academic career of faculty is pointed out (Holley, 2018 ; Tseng et al., 2023 ). Academic placement is one of the important aspects of academic career, which is closely related to faculty hiring and employment (Zheng et al., 2022 ; Zhu and Yan, 2017 ). The initial placement of doctoral students will be influenced by the learning experience during the doctoral stage and the research networks at the time of the appointment (Kaslow et al., 2018 ; Yang et al., 2022 ). For university faculty, scholars have found through surveys that interdisciplinary graduates are more likely to secure academic positions than non-interdisciplinary graduates (Millar, 2013 ). The level of academic placement can be expressed by the prestige of the universities where the faculty employed, and universities’ prestige is associated with formal university rankings such as the U.S. News and World Report Best Global Universities Rankings or the Times Higher Education Ranking (Cowan and Rossello, 2018 ). Scholars divide the universities into different ranking levels to evaluate faculty post-doctoral academic placement (Smeets et al., 2006 ). However, in the aspect of multidisciplinary education, there is still a lack of statistical empirical evidence for the academic placement of faculty, although it has been paid attention to (Holley, 2018 ).

The current literature focuses on the benefits of multidisciplinary education and acknowledges its influence on the academic careers of doctoral students and faculty. And it is important to note that elite universities place more emphasis on funding support, research center construction, and faculty positions for multidisciplinary and interdisciplinary initiatives (Leahey et al., 2019 ). They are more willing to engage in high-level interdisciplinary research (Li and Yin, 2023 ), which could benefit the employment opportunities for faculty with multidisciplinary backgrounds. The elite universities have explicitly prioritized the recruitment of faculty with multidisciplinary backgrounds in recent years. Both MIT and Stanford have specifically stated in their recruiting criteria that they favor candidates with a multidisciplinary background for the 2023 recruitment drive Footnote 1 . But little is known about the academic placement situation of individuals with a multidisciplinary education background, as there is still a lack of statistical empirical evidence within literature (Holley, 2018 ).

Limited attention and the curse of multidisciplinary education

In contrast to the benefits of multidisciplinary education and its positive impact on academic placement, it is imperative to acknowledge that attention is a finite and valuable resource, and any allocation of attention comes with associated opportunity costs (Hirshleifer and Teoh, 2003 ). The issue of limited attention in managing vast amounts of information and knowledge can lead to decision biases in both individuals and organizations, often stemming from constraints in attention and processing capacity (Choi and Choi, 2019 ). Individuals’ attention is frequently susceptible to external influences such as media, and they may be easily distracted or misled (Weng et al., 2012 ).

Compared to the advantages of multidisciplinary education rooted in knowledge recombination theory, a more prevalent concern is that individuals who secure faculty positions may encounter various barriers (Boden et al., 2011 ). The multidisciplinary foundation capacitates individuals to adeptly synthesize multifaceted knowledge, leading to the genesis of novel and inventive outcomes (Arts and Fleming, 2018 ). Therefore, researchers should possess both knowledge depth (i.e., understanding of a specific field) and knowledge breadth (i.e., extent of knowledge across multiple fields) (Mannucci and Yong, 2018 ). Knowledge depth enhances an individual’s expertise in a specific field, but it is important to note that it may result in a loss of flexibility in terms of problem-solving, adaptation, and creative idea generation (Dane, 2010 ). However, blindly pursuing knowledge breadth to enhance the flexibility of knowledge structure may be susceptible to the impact of limited attention, leading to a reduction in knowledge depth and a decline in specialization. It is evident that we simply cannot process and respond to all the information and knowledge in the environment that may be relevant to our tasks (Scalf et al., 2013 ). Graduates with a multidisciplinary background express a lack of disciplinary belonging and encounter challenges (Balaban, 2018 ; O’Donovan, 2019 ). The flexibility between disciplinary knowledge and limited personal attention may lead to questions about the knowledge depth and capacity in a single discipline when applying for academic positions in academic institutions (Dane, 2010 ; Haider et al., 2018 ). While breadth of knowledge is undeniably valuable, there exists a concomitant risk of attenuating depth within specific disciplines (Arts and Fleming, 2018 ).

The theories of knowledge recombination and limited attention present conflicting perspectives on the role of multidisciplinary education in academic placement. While some studies have found that interdisciplinary graduates are more likely to secure academic positions (Millar, 2013 ), others argue that the limited attention allocation may disadvantage students and faculty with multidisciplinary education backgrounds in academic placement (Dane, 2010 ; K. A. Holley, 2018 ). As empirical evidence on the academic placement of individuals with multidisciplinary education backgrounds is lacking, this study seeks to address this dilemma by exploring two key questions: Firstly, how does a faculty member’s multidisciplinary background influence their academic placement? Secondly, does such a background contribute to an upward trajectory in their academic career?

The moderating effect of gender and academic inbreeding

The relationship between a faculty’s multidisciplinary education background and their academic placement can be influenced by various confounding factors, some of which may simultaneously affect both multidisciplinarity and placement outcomes. Previous studies have suggested that several other variables can influence individual academic placements, including academic titles, international mobility status (Ryazanova and McNamara, 2019 ), academic productivity (Fontana et al., 2020 ; Rosman et al., 2020 ), faculty’s H-index (Fontana et al., 2022 ), and various other factors. These elements collectively contribute to the intricate landscape of academic placement.

In particular, the roles of gender and academic inbreeding in this context deserve examination, as they intersect with multidisciplinarity in complex ways. Gender disparity remains palpable within the scientific workforce, with females often encountering professional barriers (Chubb and Derrick, 2020 ; Huang et al., 2020a , 2020b ). The academic world has been a subject of discourse regarding the proverbial “glass ceiling” that female academicians face. The dynamics of these interactions, especially when intertwined with multidisciplinarity, have been relatively underexplored.

Research has shown that women tend to be in a relatively disadvantaged position both in terms of academic publications and in the workplace compared to their male counterparts (Chubb and Derrick, 2020 ; Huang et al., 2020a , 2020b ). Interestingly, studies have found that female scientists are more inclined to transcend disciplinary boundaries than their male peers (Rhoten and Pfirman, 2007 ). While there is limited literature examining whether the academic success of individuals with a multidisciplinary background differs between males and females, it is plausible to assume that gender can indeed influence both multidisciplinary education backgrounds and individual placements, given the existing gender bias in academia (Lundine et al., 2019 ).

Additionally, academic inbreeding, which refers to the practice of institutions hiring their own graduates, can have adverse effects on multidisciplinary studies. Academic inbreeding has been shown to inhibit the influx of new and fresh ideas (Horta, 2022 ; Mazzoleni et al., 2021 ). Studies suggest that faculty engaged in academic inbreeding tend to maintain research subjects throughout their careers, potentially increasing the risks associated with multidisciplinarity and weakening creativity among faculty (Morichika and Shibayama, 2015 ). However, despite these risks, the practice of universities hiring their own graduates still persists (Altbach et al., 2015 ). This practice can circumvent concerns about the depth of knowledge and research flexibility for faculty members with multidisciplinary backgrounds. Consequently, academic inbreeding is likely to exert an influence on both faculty members’ choices regarding multidisciplinarity and their academic placements.

Taken together, we specifically explore the moderating effect of gender and academic inbreeding on the relationship between multidisciplinary background and academic placement and progression in this study.

Data and methodology

Data procurement and assimilation.

This study employed a Curriculum Vitae (CV) analytical methodology. Our data collection process began with a systematic web scraping operation, aimed at extracting faculty CVs from the official portals of universities worldwide. The gathered data was then carefully curated and converted into a format suitable for sophisticated statistical analyses.

For the foundational framework of our data collection, we referred to the 2022 U.S. News World University Rankings Footnote 2 . This involved a comprehensive evaluation of the top 1,000 universities as ranked in this list. Employing a stratified sampling method, our selection included 183 universities, ensuring a representative cross-section that spans a broad spectrum of ranking categories. We operationalized faculty rankings—encompassing undergraduate, graduate, and placement levels—using the 2022 U.S. News World University Rankings as our standard metric.

Our extensive, cross-national data gathering encompassed six key countries: the United States, Canada, the United Kingdom, Australia, New Zealand, and Singapore. This broad scope encompassed faculty members with doctoral degrees awarded from 1973 to 2022. This meticulous process resulted in the compilation of ~500,000 faculty CVs. These CVs provided crucial data points such as gender, academic titles, and disciplinary affiliations, in addition to yielding valuable insights into their academic and professional journeys.

To enhance the robustness and comprehensiveness of our dataset, we incorporated publication metrics, extracting publication counts, citations, and H-index of faculty members up to March 2023 from the Scopus database. This integration provided a comprehensive view of each faculty member’s academic productivity.

For the purpose of this study, we systematically categorized the faculty’s educational background and disciplinary focuses. Following Biglan’s classification framework, we divided disciplines into eight distinct categories, based on three critical dimensions: hard/soft, pure/applied, and life/nonlife. A detailed breakdown of these categories is presented in Table 1 .

Dependent variable

In alignment with the existing literature (Cowan and Rossello, 2018 ; Yang et al., 2022 ), the study’s dependent variables encompass “Placement of University Faculty”, “Upward Success in Placement Compared to Undergraduate”, and “Upward Success in Placement Compared to Graduate”.

“Placement of University Faculty” is defined by the faculty’s current institutional affiliation as ranked in the 2022 U.S. News World University Ranking. This ranking is utilized as a concrete indicator of the faculty’s academic standing and accomplishments.

“Upward Success in Placement Compared to Undergraduate” and “Upward Success in Placement Compared to Graduate” are conceptualized as binary indicators. A value of 1 indicates that the faculty’s current institutional ranking exceeds that of their undergraduate or graduate institutions, respectively. In contrast, a value of 0 indicates no advancement or a decline in their placement relative to their previous institutions. It is important to note that the terms “graduate stage” or “graduate university” refer specifically to master’s and doctoral levels of education. In cases where faculty proceeded directly to doctoral studies, the data from these doctoral engagements were prioritized.

To control for the impact of outliers, a truncation approach was applied to the variables related to university rankings, trimming data points beyond the 5th and 95th percentiles. This method was employed to ensure a more representative dataset and to refine the precision of the analysis.

Independent variable

The essence of this research is the examination of the consequences of faculty’s multidisciplinary backgrounds. Employing Biglan’s framework, faculty disciplines were classified at three academic stages: undergraduate, graduate, and placement. A disciplinary shift between any of these stages was considered indicative of a multidisciplinary background.

This multidisciplinary background was further dissected into three components: “Multidisciplinary Frequency”, “Temporal Multidisciplinary Shifts”, and “Nature of Multidisciplinary Transition”, each reflecting aspects of faculty multidisciplinarity.

“Multidisciplinary Frequency” measures the number of times a faculty member changes disciplines throughout their academic journey, ranging from 0 (no change) to 2 (two changes).

“Temporal Multidisciplinary Shifts” assesses the timing of these disciplinary transitions, categorized as: 0 for no transition, 1 for a transition between undergraduate and graduate stages, 2 for a shift from graduate to placement, and 3 for transitions occurring at both stages.

Lastly, “Nature of Multidisciplinary Transition” delineates the type of disciplinary transition, such as a shift from a nonlife discipline (e.g., computer science) to a life discipline (e.g., biology). This led to the identification of six distinct transition types, each encoded as a binary variable to denote the presence or absence of a specific transition type.

Moderating variables

Gender: This variable is represented as a binary indicator, where a value of 1 denotes male faculty, and 0 indicates female faculty.

Academic Inbreeding: This binary variable distinguishes between faculty who have secured their placement within their alma mater (either graduate or undergraduate institutions) and those who have ventured to external institutions. A value of 1 is assigned to inbred faculty, while a 0 signifies external placement (Kwiek and Roszka, 2022 ).

Control variables

To bolster the rigor of our analyses, several control variables were incorporated:

Faculty’s Academic Titles: This ordinal variable reflects academic seniority, classified into four levels. The categorization ranges from full professors (0), to associate professors (1), assistant professors/lecturers (2), and postdoctoral/other academic personnel (3), providing a hierarchical representation of academic positions (Sherman and Tookes, 2022 ).

International Mobility: Encoded as a binary variable, this factor accounts for the faculty’s international academic exposure. A value of 1 indicates international mobility, while 0 represents solely domestic academic experiences (Ryazanova and McNamara, 2019 ).

Academic Productivity: In line with extant literature (Liang et al., 2022 ; Waltman, 2016 ), academic productivity was measured using two indices: total publications and publication quality (total citations). Total publications quantify the aggregate number of scholarly articles published by a faculty member within a specific timeframe, whereas publication quality evaluates citations received by these publications. Both indices were sourced from the Scopus database up to March 2023.

Academic Influence: Drawing from established metrics (Fontana et al., 2022 ; Hirsch, 2005 ), the H-index was utilized as an author-level metric to assess the cumulative academic influence of individual researchers. The H-index data for each faculty member was also extracted from the Scopus database until March 2023.

In our primary estimations, along with the aforementioned control variables, gender and academic inbreeding were also accounted for to ensure robust estimations. Moreover, fixed effects pertaining to the faculty’s current geographic location and the year of their doctoral graduation were incorporated. This was done to ensure the robustness of our estimations and to control for time-invariant geographical variations and time-variant academic age-related differences (Kwiek and Roszka, 2022 ). This approach allows for a more granular analysis by considering regional and temporal variations.

Analytical approach

In addressing the fundamental research inquiries concerning the implications of faculty members’ multidisciplinary backgrounds on their academic progressions, we deployed a comprehensive analytical framework.

To commence, we employed Ordinary Least Squares (OLS) regressions to gauge the impact of multidisciplinary backgrounds on faculty placement. This initial inquiry was further complemented by specific subgroup OLS regressions, which meticulously examined the effect of multidisciplinary background on the different phases of faculty members’ educational journeys, encompassing their undergraduate, graduate, and placement stages. These academic institutions’ rankings were subsequently stratified into distinct categories, following the classifications of previous studies (Leahey et al., 2019 ; Smeets et al., 2006 ), including the top 50, 51–100, 101–200, 201–300, 301–500, and 501 onwards.

To attain a more nuanced comprehension of the phenomenon, we integrated Quartile Regression techniques, providing elucidation on how multidisciplinary backgrounds exerted influence across diverse strata of university ranking tiers. Subsequently, we embarked on an exploration of the potential moderating effects of gender and academic inbreeding on the complex nexus between multidisciplinary backgrounds and faculty placements. The investigative journey culminated with a granular examination of the intricacies surrounding the timing and the specific nature of disciplinary transitions.

Transitioning to our second core inquiry—namely, whether a multidisciplinary background facilitates upward mobility in faculty academic progression—we embarked on a phased analytical expedition. Logit regressions formed the cornerstone of this phase, systematically evaluating the correlations between multidisciplinary backgrounds and the trajectories of upward academic progression. This analysis was further fortified by subgroup logit analyses, which delved into the dynamics of rank-centric changes across the undergraduate, graduate, and placement phases of faculty members’ academic careers. Subsequent layers of analysis delved into the moderating roles played by gender and academic inbreeding within this context. The analytical suite concluded with a meticulous exploration of how both the timing and the nature of multidisciplinary transitions intersected with faculty members’ prospects for upward mobility.

Descriptive analysis and correlation

Table 2 delineates descriptive nuances in detail. Within the examined sample, an intriguing revelation is that 39.06% of faculty have navigated through a multidisciplinary trajectory. Dissecting this further, 28.71% transitioned disciplines once, while a more selective 10.36% did so twice. Among all of the faculty, 14.92% experienced a disciplinary shift during their transition from undergraduate to graduate studies, 13.79% encountered a similar transition during the shift from the graduate stage to their current placement, and 10.36% underwent disciplinary transitions at both stages Footnote 3 .

Table 3 outlines the correlations between various studied variables. There is a modestly positive correlation ( r  = 0.045) observed between multidisciplinarity and faculty placement rankings, indicating a relationship between diverse academic backgrounds and higher institutional affiliations. However, there appears to be no significant correlation between the frequency of disciplinary transitions and upward mobility from undergraduate levels. In contrast, a slightly negative correlation ( r  = −0.061) is noted between the frequency of disciplinary changes and upward mobility from graduate institutions.

Figure 1 graphically depicts the relationship between the rankings of undergraduate, doctoral, and placement universities for the faculty. It shows that faculty members, on average, originated from undergraduate institutions ranked at 240 and completed their doctoral studies at institutions ranked at 198. Interestingly, their eventual placement was predominantly in universities with an average rank of 259. This observation underscores a tendency for faculty placements to marginally fall below the rankings of both their undergraduate and doctoral institutions.

The yellow bars refer to the average undergraduate university ranking of university faculty, while the brown bars indicate the average graduate university ranking of university faculty. The horizontal axis represents the ranking group of the placement of university faculty. The vertical axis indicates the average university ranking.

Multidisciplinary background and placement

Baseline estimations.

Our initial analytical endeavor aims to discern the potential influence of multidisciplinarity on faculty placements. Recognizing that multidisciplinarity spans from 0 to 2 disciplinary transitions, we operationalized it as a categorical entity in foundational regressions to assess any variability when conceptualized continuously. The results of these OLS regressions are elucidated in Table 4 .

Models (1) and (2) scrutinize multidisciplinary frequency as a continuous construct concerning faculty placement. Following the integration of auxiliary variables and fixed effects in models (2) and (4), a discernible elevation in the multidisciplinary coefficient is observed, increasing from 26.904 to 29.260, signifying statistical significance at the 0.01 threshold. When portrayed categorically in Models (3) and (4), multidisciplinary frequency maintains a similar statistical stature. Notably, experiencing two disciplinary transitions registers more substantial coefficients ( β  = 67.253; β  = 69.228) relative to a solitary transition ( β  = 12.463; β  = 17.081). It is crucial to highlight that due to the reverse scale of university rankings, a positive coefficient implies an inverse influence on the ranking outcome, indicating that multidisciplinary background relates to less prestigious placement, while the more frequent of disciplinary transitions, the more severe the inhibitive effect on current placement.

Further analyses

In our subsequent analytical phase, we segmented the faculty sample based on the respective undergraduate, graduate, and placement university rankings. Table 5 presents the outcomes of these stratified regressions, with all models incorporating relevant control variables and fixed effects.

Models (1)–(6) are dedicated to the subset of undergraduate university rankings. A recurrent theme that emerges across these models is the adverse impact of a multidisciplinary background on faculty members’ academic placements. This detrimental trend is particularly pronounced for faculty members who completed their undergraduate education at institutions of lesser prestige, specifically for universities ranked between 301–500 ( β  = 25.980, p  < 0.001), as illustrated in Model (5). The impact is even more significant for universities ranked below the 500th position ( β  = 59.879, p  < 0.001), as depicted in Model (6).

The subsequent set of models, Models (7)–(12), corroborate the earlier findings. They suggest that multidisciplinarity appears to have a detrimental effect on achieving top-tier faculty placements, with statistical significance observed at the 0.01 threshold. Moreover, the adverse effect is amplified for faculty members whose graduate universities are ranked below 300.

Shifting our focus to Models (13)–(18), which are contingent on faculty members’ placement university rankings, the effect of multidisciplinarity manifests with greater complexity. A consistent pattern with the earlier results emerges; multidisciplinary backgrounds appear to hinder academic placements, particularly for universities ranked below the 200th position. However, for elite universities, a different narrative unfolds. Multidisciplinary backgrounds are associated with improved placements, as indicated in Models (13) to (15), especially for global top 50 universities and institutions ranking between 101 and 200. In these cases, multidisciplinarity can be considered a distinct advantage in the placement process.

To further dissect the nuanced influence of multidisciplinarity across diverse university rank strata, we employed quantile regression techniques. Figure 2 crystallizes this variance across the 5th to 95th quartiles, with all coefficients reflecting significance at the 0.05 level. The multifaceted character of multidisciplinarity’s influence is evident, especially noting the 5th and 25th quartiles, registering values of −0.000 and −2.150. The central 50th quartile presents a coefficient of 14.919, encapsulating an initial rise followed by a subsequent decline across quartile gradations. Conclusively, Fig. 2 underscores a perceptible multidisciplinary inflection in the elite 30% of global universities. To summarize succinctly, faculty members with multidisciplinary backgrounds are notably well-positioned for placements in prestigious institutions, while their prospects appear to diminish for universities ranked below the top 30% threshold.

The black straight lines represent the average estimate and the 95% confidence interval (CI). The blue lines and shadows are multidisciplinary background’s influence at different quantiles of faculty placement ranking and the 95% confidence interval (CI).

We further delved into the moderating effects of gender and academic inbreeding on the relationship between multidisciplinarity and academic placement. The analytical outcomes of these interactions are meticulously delineated in Table 6 .

Models (1) and (3) report the interaction of multidisciplinary and gender on academic placement, with model (3) incorporate more control variables and fixed effects. As suggested in Model (3), the coefficient for “Multidisciplinary*Gender” interaction stands at −15.984 ( p  < 0.01), This finding underscores a mitigated adverse effect of multidisciplinarity for male faculty members when compared to their female counterparts. Similarly, Models (2) and (4) report the interaction of multidisciplinary and inbreeding on academic placement, with model (4) incorporate more control variables and fixed effects. Model (4) reveals a coefficient of −10.187 ( p  < 0.05), for “Multidisciplinary*Inbreeding”, suggesting a mitigated adverse impact of multidisciplinarity for academically inbred faculty members in comparison to their non-inbred peers.

The occurrence timing and nature of multidisciplinarity on academic placement

The emergent narrative of multidisciplinarity’s less-than-favorable impact on faculty academic placement precipitates deeper inquiries: How do the occurrence timing and nature of disciplinary shifts influence academic placement? Table 7 presents a stratified exploration, with independent variables capturing both the timing and nature of these shifts. Models (1) and (2) pivot around the “Temporal Multidisciplinary Shifts”, while Models (3) and (4) gravitate towards transitions during the “Undergraduate to Graduate” phase. Lastly, Models (5) and (6) pivot around the ‘Graduate to Placement’ stage. In each pairing, the even-numbered models incorporate essential controls and fixed effects.

Model (2) accentuates the constructive role of multidisciplinary transitions during the “Graduate to Placement” stage in enhancing academic placements, demonstrating statistical significance at the 0.1 level. This suggests a heightened receptivity to multidisciplinarity during this juncture compared to the undergraduate-graduate transition. Model (4) demarcates the effects of three specific transition types—“Hard-Soft”, “Applied-Pure”, and “Life-Nonlife”—and indicates that these transitions tend to lead to less favorable academic placements. This is contrary to expectations and warrants further investigation. Contrarily, Model (6) manifest that while all six types of transitions are statistically significant, only the “Pure-Applied” transition during the shift from graduate education to placement, with a coefficient of −33.679, correlates with enhanced placement potential. This specific transition appears to offer a notable advantage in academic placements. However, the remaining five typologies present mixed outcomes, potentially impacting placement prospects, particularly in top-tier institutions.

Multidisciplinary background and upward mobility in academic career

While our core analysis may have tempered the perceived advantages of multidisciplinary transitions for faculty placements, it is essential to underscore that such shifts often align with periods of professional transition, frequently oriented toward enrollment or placement. Consequently, delving into the influence of multidisciplinarity on faculty members’ ascent in university rankings provides a nuanced perspective on academic mobility. Table 8 presents the results derived from a logit regression, examining the trajectory of multidisciplinary transitions in the context of upward mobility relative to undergraduate and graduate phases. To ensure a meticulous comparative framework, multidisciplinarity was assessed both as a categorical and continuous variable. The inclusion of control variables and fixed effects enhances the robustness of these models.

Model (1) surveys the implications of continuous multidisciplinary metrics on faculty’s upward mobility compared to undergraduate benchmarks and fails to manifest statistical significance. However, a shift to categorical metrics in Model (2) unravels nuanced outcomes. Singular disciplinary transitions yield a positive coefficient, promoting upward mobility, whereas dual transitions register a reverse effect, inhibiting such advancements. As we transition to models (3) and (4), which focus on graduate benchmarks, a pronounced detrimental undertone emerges for multidisciplinarity, irrespective of its categorical or continuous incarnation. The inhibitory effect seems accentuated with more frequent disciplinary oscillations.

Further analysis

In our subsequent analytical phase, we mirrored previous analyses, also segmenting the faculty sample based on the respective undergraduate, graduate, and placement university rankings. Table 9 presents the outcomes of these stratified regressions, investigating the effect of multidisciplinarity on upward mobility when compared to the undergraduate stage as the benchmark. All models incorporate relevant control variables and fixed effects.

Models (1)–(6) focus on the subset of undergraduate university rankings. A recurring theme that emerges across these models is the adverse impact of a multidisciplinary background on the likelihood of faculty members’ upward mobility when compared to their undergraduate university as the benchmark.

The subsequent set of models, Models (7)–(12), corroborates the earlier findings. These models suggest that multidisciplinarity appears to have a detrimental effect on the possibility of upward mobility in placement compared to the undergraduate stage as the benchmark. This effect is particularly pronounced for faculty members whose graduate universities are ranked below 500.

Shifting our focus to Models (13)–(18), which are contingent on faculty members’ placement university rankings, the effect of multidisciplinarity is different. A consistent pattern shows that multidisciplinary backgrounds appear to increase the possibility of faculty member’s upward mobility compared to their undergraduate education as the benchmark, particularly for universities ranked between 101 and 200.

Table 10 , which juxtaposes the results in Table 9 , extends this exploration by using the faculty’s graduate university as the benchmark. Across models (1) to (12), a recurring theme is the detrimental influence of multidisciplinarity on the likelihood of upward mobility when compared to the graduate stage, albeit with minor variations.

Conversely, as we progress to models (13)–(18), the narrative becomes more heterogeneous. While some segments emphasize the previously mentioned deleterious effects, others, especially Model (18), reveal a latent potential for multidisciplinarity to catalyze faculty members’ upward mobility, particularly for those in universities ranked below 500.

Juxtaposing previous results, Table 11 elucidates the logit regression outcomes for the moderating effect of gender and academic inbreeding on the relationship between multidisciplinary and upward mobility. Ensuring analytical rigor, the models are meticulously fortified with pertinent control variables and fixed effects.

Interestingly, while the “Multidisciplinary*Gender” interaction yields statistically insignificant results across the models, the “Multidisciplinary*Inbreeding” interaction manifests significance at the 0.05 level in models (2) and (4) with coefficients of 0.111 and 0.110, respectively. This suggests that academic inbreeding offers a cushioning effect against the adverse effect of multidisciplinarity, attenuating its impact on upward mobility. In essence, for inbred academics, the multidisciplinary trajectory becomes less of an impediment.

The occurrence timing and nature of multidisciplinarity on the upward success

Given the nuanced adverse implications of multidisciplinarity on upward mobility, it becomes imperative to interrogate whether the specific chronology and nature of disciplinary transitions play pivotal roles. Table 12 outlines these dynamics, with independent variables centered on the “Temporal Multidisciplinary Shifts” and the specific “Nature of Multidisciplinary Transition”. For comprehensive insights, control variables and fixed effects are integrated.

Model (1) underscores the salience of multidisciplinary transitions occurring during the Graduate to Placement phase, fostering faculty’s upward mobility ( β  = 0.152) at the 0.01 significance level. In model (2), while specific disciplinary shifts during the undergraduate to graduate phase—specifically “Pure-Applied” and “Nonlife-Life”—emerge as potential catalysts for upward mobility ( β  = 0.102; β = 0.105), the “Life-Nonlife” transition appears counterproductive ( β  = −0.235). Model (3) further reinforces the efficacy of the “Pure-Applied” transition while spotlighting potential impediments inherent in “Hard-Soft”, “Nonlife-Life”, “Applied-Pure”, and “Life-Nonlife” transitions.

Pivoting to the context of Graduate to Placement phase in model (4), an overarching detrimental undertone for multidisciplinary transitions surfaces ( β  = −0.220). Furthermore, model (5) accentuates the merits of the “Pure-Applied” transition, while other disciplinary oscillations predominantly emerge as hurdles. Conclusively, model (6) reinforces the deleterious implications of specific disciplinary transitions, intensifying the narrative of multidisciplinarity’s intricate relationship with upward mobility.

Employing Biglan’s classification of academic disciplines, this research analyzed the influence of university faculty’s multidisciplinary backgrounds on their academic placement and career progression within universities. The study also investigated the effects of the timing and nature of disciplinary transitions made by faculty members. Utilizing data extracted from 500,000 publicly available curricula vitae (CV) of university faculty covering six countries, we conducted a finer-grained analysis. The results revealed that a multidisciplinary background has a generally negative impact on the placement and career advancement of faculty members in academic settings. However, it is essential to note that the effects of disciplinary transitions on career outcomes vary depending on their stage and specific type. Moreover, the analysis indicated that the adverse consequences of possessing a multidisciplinary background are mitigated in cases of male faculty members and those with a history of academic inbreeding.

This nuanced approach highlights the complex interplay between faculty members’ educational backgrounds and their career trajectories, underscoring the importance of considering both individual and institutional factors in academic career development.

Theoretical implication

This study significantly enriches the discourse on multidisciplinary education, with a specific focus on its implications for academic faculty placement. The theoretical contributions are multifaceted.

First, this study recontextualized Biglan’s classification. By applying Biglan’s classification to the analysis of faculty educational backgrounds, this research extends the utility of this well-regarded disciplinary classification scheme (Hudson et al., 2023 ; Marbach-Ad et al., 2019 ; Paulsen and Wells, 1998 ; Simpson, 2017 ). Prior literature usually applied Biglan’s classification in explaining the role of multidisciplinary courses on students’ academic performance and post-graduation achievements (O’Donovan, 2019 ; Tseng et al., 2023 ). The study moves beyond the traditional application of Biglan’s classification schema, demonstrating its relevance not only in categorizing disciplines but also in providing a nuanced understanding of the implications of faculty’s educational backgrounds in both life and non-life disciplines. This novel application underscores the versatility of Biglan’s framework in new contexts of multidisciplinary education (Rosman et al., 2020 ).

Second, this study lends empirical support to the Limited Attention Theory (LAT), suggesting that a multidisciplinary education background may impede faculty from obtaining optimal placements. This finding aligns with concerns in academia about the breadth-over-depth approach inherent in multidisciplinary education (Arts and Fleming, 2018 ; Balaban, 2018 ; Haider et al., 2018 ; Tseng et al., 2023 ; Wright and Vanderford, 2017 ), contributing to the ongoing debate on the balance between specialization and interdisciplinary learning in higher education.

Third, our findings illustrate the practical implications of the Knowledge Recombination Theory (KRT). We demonstrate that multidisciplinary backgrounds are viewed favorably in elite academic institutions. This observation validates the theory’s premise that integrating knowledge from diverse fields is beneficial and sought after (Fontana et al., 2022 ; Petersen et al., 2021 ; Xiao et al., 2022 ), especially in top-tier universities (Leahey et al., 2019 ; Li and Yin, 2023 ). This observation echoes the theory’s premise, highlighting the specific contexts where multidisciplinarity is particularly advantageous.

This research also reveals the duality in the impact of multidisciplinary backgrounds. While general university settings may view multidisciplinary backgrounds as less favorable (consistent with LAT), elite universities appreciate these backgrounds (aligned with KRT). This discrepancy signifies a divergence in recruitment strategies, indicating a progressive shift in elite institutions (Holley, 2018 ; Huang et al., 2020a , 2020b ), such as the 2023 recruitment at MIT and Stanford.

Last but not least, this research sheds light on the influence of gender and academic inbreeding within the sphere of multidisciplinary education. The study reveals that male faculty members and those with an academic inbreeding background can counterbalance the potential disadvantages of a multidisciplinary education (Lundine et al., 2019 ; Huang et al., 2020a , 2020b ; Rhoten and Pfirman, 2007 ). Our findings suggest that inbred faculty not only offset the unfavorable impact of multidisciplinary backgrounds on their chances of getting superior placement, but they also buffer the negative consequences of achieving upward mobility on academic placement. Although academic inbreeding is not regarded as beneficial, especially in terms of knowledge innovation (Horta, 2022 ; Morichika and Shibayama, 2015 ), previous studies find that it still plays a negligible role in the faculty placement (Altbach et al., 2015 ). Our results support these findings, and contribute to the discourse on gender inequality and the role of academic inbreeding in academia.

In summary, this study enhances the understanding of how multidisciplinary backgrounds influence academic placements and trajectories, offering new insights into the interplay of educational background, gender, and academic traditions in higher education. It prompts a reevaluation of multidisciplinary background in academia, especially in the context of faculty career advancement and placement strategies.

Practical implication

This study provides a roadmap for both academic institutions and individuals, highlighting the strategic importance of multidisciplinary education in faculty recruitment and career development within the complex landscape of higher education. This research offers valuable insights for universities and their recruitment committees, illuminating the complex dynamics of faculty placement associated with multidisciplinary academic backgrounds. The study underscores the importance of recognizing that the benefits of multidisciplinarity are not universally applicable, and are influenced by factors such as the nature and timing of disciplinary transitions. Elite academic institutions, which often spearhead cutting-edge research, may find particular value in embracing faculty with diverse academic backgrounds. These faculty members can contribute to an environment of broadened perspectives and enhanced innovation. Interestingly, our findings also reframe the conversation around academic inbreeding. Traditionally viewed in a negative light, academic inbreeding may, in fact, provide a buffer against the less favorable aspects of a multidisciplinary background. This nuanced understanding could guide institutions in their recruitment and promotion strategies, considering the potential advantages of academic inbreeding alongside its more recognized drawbacks.

For individual faculty, the strategic decision-making process regarding disciplinary transitions is crucial. Our study reveals that while a multidisciplinary path can facilitate entry into prestigious institutions, it may also present obstacles in certain contexts, particularly when interwoven with other variables such as gender and academic heritage. Early-career researchers should be particularly cognizant of the strategic timing and nature of their disciplinary shifts. Our findings suggest that transitioning disciplines during the early stages of academic training, specifically from undergraduate to graduate levels, can be advantageous for academic placement. Moreover, moving from pure to applied disciplines seems to not only enhance prospects of better placement but also aid in ascending the academic ladder. For faculty members originating from non-elite institutions, cultivating a multidisciplinary profile emerges as a promising avenue for career advancement. To maximize their potential for upward mobility within elite academic settings, individuals should approach discipline transitions with careful consideration, ideally beginning as early as their undergraduate education. A deliberate shift towards applied disciplines during this phase could lay the groundwork for a distinguished academic trajectory.

Conclusion, limitation, and future research

Using data from university faculty CVs, this study analyses the effects of multidisciplinary educational backgrounds on faculty placement and academic profession within Biglan’s Discipline Classification. The findings highlight the difficulties that multidisciplinary backgrounds offer faculty in obtaining better placement and achieving upward mobility. It also responds to the application of knowledge recombination and limited attention theory in multidisciplinary and interdisciplinary education. It is crucial to note, however, that the impact of discipline transitions is different based on the occurrence timing and types of the transition. Furthermore, gender and academic inbreeding factors help to reduce the negative impact of multidisciplinary backgrounds. This research not only adds to the current body of research but also advances the multidisciplinary discussion.

However, it is critical to recognize the study’s shortcomings. One major limitation is the data collection and refinement process. The dataset currently only includes faculty from six countries: the United States, Canada, Australia, New Zealand, the United Kingdom, and Singapore. As a result, larger inclusion could improve the sample’s national representativeness. Furthermore, the creation and maintenance of a comprehensive, global CV database necessitate significant effort and time investment. Factors such as faculty demographics and the family’s economic position are not easy to get throughout the CV data preparation process. Notably, the biographical section of the CVs was left out of this study because its comprehensive examination requires the involvement of a natural language model for further data refining and extraction. Future research efforts could considerably improve the representativeness of faculty CV data by increasing the breadth of nations and universities from which CV data is gathered. Furthermore, using natural language processing techniques to improve the personal autobiography part could provide deeper insights into faculty’s career mobility experiences. This would allow for a more thorough evaluation of the long-term impact of a multidisciplinary background on academic profession.

In conclusion, this study unveils the multifaceted effects of multidisciplinarity on academic placements and career trajectories. While multidisciplinary backgrounds can be a double-edged sword, their potential benefits or pitfalls are not set in stone but depend on a myriad of factors. As academia continues to evolve, understanding these dynamics becomes ever more crucial for institutions, policymakers, and academics alike.

Data availability

The data used to support the findings of this study were obtained from public domain Scopus databases and faculty CVs available on universities’ public websites.

For further information regarding MIT’s Digital Learning Lab’s and the Stanford Centre for Biomedical Ethics’ 2023 hiring requirements, please visit https://careers.peopleclick.com/careerscp/client_mit/external/jobDetails/jobDetail.html?jobPostId=24980&localeCode=en-us and https://facultypositions.stanford.edu/en-us/job/493558/stanford-center-for-biomedical-ethics-academic-scholar .

For further information about the 2022 U.S.NEWS World University Rankings, please visit https://www.usnews.com/education/best-global-universities/rankings .

Supplementary Figure S1 in this study includes a Sankey diagram indicating disciplines transitions among faculty across their undergraduate, graduate, and placement stages.

Altbach PG, Yudkevich M, Rumbley LE (2015) Academic inbreeding: local challenge, global problem. Asia Pac Educ Rev 16(3):317–330. https://doi.org/10.1007/s12564-015-9391-8

Article   Google Scholar  

Arts S, Fleming L (2018) Paradise of novelty—or loss of human capital? Exploring new fields and inventive output. Organ Sci 29(6):1074–1092. https://doi.org/10.1287/orsc.2018.1216

Balaban C (2018) Mobility as homelessness: the uprooted lives of early career researchers. Learn Teach 11(2):30–50. https://doi.org/10.3167/latiss.2018.110203

Biglan A (1973) Relationships between subject matter characteristics and the structure and output of university departments. J Appl Psychol 57(3):204–213. https://doi.org/10.1037/h0034699

Boden D, Borrego M, Newswander LK (2011) Student socialization in interdisciplinary doctoral education. High Educ 62(6):741–755. https://doi.org/10.1007/s10734-011-9415-1

Chan KL, Chin DCW, Wong MS, Kam R, Chan BSB, Liu C-H, Wong FKK, Suen LKP, Yang L, Lam SC, Lai WW, Zhu X (2022) Academic discipline as a moderating variable between seating location and academic performance: implications for teaching. High Educ Res Dev 41(5):1436–1450. https://doi.org/10.1080/07294360.2021.1928000

Choi S, Choi WY (2019) Effects of limited attention on investors’ trading behavior: evidence from online ranking data. Pac-Basin Financ J 56:273–289. https://doi.org/10.1016/j.pacfin.2019.06.007

Chubb J, Derrick GE (2020) The impact a-gender: gendered orientations towards research Impact and its evaluation. Palgrave Commun 6(1):1. https://doi.org/10.1057/s41599-020-0438-z

Costa AR, Ferreira M, Barata A, Viterbo C, Rodrigues JS, Magalhães J (2019) Impact of interdisciplinary learning on the development of engineering students’ skills. Eur J Eng Educ 44(4):589–601. https://doi.org/10.1080/03043797.2018.1523135

Cowan R, Rossello G (2018) Emergent structures in faculty hiring networks, and the effects of mobility on academic performance. Scientometrics 117(1):527–562. https://doi.org/10.1007/s11192-018-2858-8

Dane E (2010) Reconsidering the trade-off between expertise and flexibility: a cognitive entrenchment perspective. Acad Manag Rev 35(4):579–603. https://doi.org/10.5465/amr.35.4.zok579

Dwivedi YK, Kshetri N, Hughes L, Slade EL, Jeyaraj A, Kar AK, Baabdullah AM, Koohang A, Raghavan V, Ahuja M, Albanna H, Albashrawi MA, Al-Busaidi AS, Balakrishnan J, Barlette Y, Basu S, Bose I, Brooks L, Buhalis D, Wright R (2023) Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. Int J Inf Manag 71:102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642

Ehls D, Polier S, Herstatt C (2020) Reviewing the field of external knowledge search for innovation: theoretical underpinnings and future (re-)search directions. J Prod Innov Manag 37(5):405–430. https://doi.org/10.1111/jpim.12549

Feng X, Ylirisku S, Kähkönen E, Niemi H, Hölttä-Otto K (2023) Multidisciplinary education through faculty members’ conceptualisations of and experiences in engineering education. Eur J Eng Educ 48(4):707–723. https://doi.org/10.1080/03043797.2023.2185126

Fontana M, Iori M, Leone Sciabolazza V, Souza D (2022) The interdisciplinarity dilemma: public versus private interests. Res Policy 51(7):104553. https://doi.org/10.1016/j.respol.2022.104553

Fontana M, Iori M, Montobbio F, Sinatra R (2020) New and atypical combinations: an assessment of novelty and interdisciplinarity. Res Policy 49(7):104063. https://doi.org/10.1016/j.respol.2020.104063

Frodeman R (2010) The Oxford handbook of interdisciplinarity . Oxford University Press

Haider LJ, Hentati-Sundberg J, Giusti M, Goodness J, Hamann M, Masterson VA, Meacham M, Merrie A, Ospina D, Schill C, Sinare H (2018) The undisciplinary journey: early-career perspectives in sustainability science. Sustain Sci 13(1):191–204. https://doi.org/10.1007/s11625-017-0445-1

Article   PubMed   Google Scholar  

Hero L-M, Lindfors E (2019) Students’ learning experience in a multidisciplinary innovation project. Educ + Train 61(4):500–522. https://doi.org/10.1108/ET-06-2018-0138

Hirsch JE (2005) An index to quantify an individual’s scientific research output. Proc Natl Acad Sci 102(46):16569–16572. https://doi.org/10.1073/pnas.0507655102

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Hirshleifer D, Teoh SH (2003) Limited attention, information disclosure, and financial reporting. J Acc Econ 36(1):337–386. https://doi.org/10.1016/j.jacceco.2003.10.002

Holley K (2009) The challenge of an interdisciplinary curriculum: a cultural analysis of a doctoral-degree program in neuroscience. High Educ 58(2):241–255. https://doi.org/10.1007/s10734-008-9193-6

Holley KA (2018) The longitudinal career experiences of interdisciplinary neuroscience PhD recipients. J High Educ 89(1):106–127. https://doi.org/10.1080/00221546.2017.1341755

Horta H (2022) Academic inbreeding: academic oligarchy, effects, and barriers to change. Minerva 60(4):593–613. https://doi.org/10.1007/s11024-022-09469-6

Huang J, Gates AJ, Sinatra R, Barabási A-L (2020a) Historical comparison of gender inequality in scientific careers across countries and disciplines. Proc Natl Acad Sci 117(9):4609–4616. https://doi.org/10.1073/pnas.1914221117

Huang Y-M, Hsieh MY, Usak M (2020b) A multi-criteria study of decision-making proficiency in student’s employability for multidisciplinary curriculums. Mathematics 8(6):6. https://doi.org/10.3390/math8060897 . Article

Article   CAS   Google Scholar  

Hudson B, Gericke N, Olin-Scheller C, Stolare M (2023) Trajectories of powerful knowledge and epistemic quality: Analysing the transformations from disciplines across school subjects. J Curric Stud 55(2):119–137. https://doi.org/10.1080/00220272.2023.2182164

James Jacob W (2015) Interdisciplinary trends in higher education. Palgrave Commun 1(1):1. https://doi.org/10.1057/palcomms.2015.1 . Article

Kaslow NJ, Bangasser DA, Grus CL, McCutcheon SR, Fowler GA (2018) Facilitating pipeline progress from doctoral degree to first job. Am Psychol 73(1):47–62. https://doi.org/10.1037/amp0000120

Kearney E, Gebert D (2009) Managing diversity and enhancing team outcomes: the promise of transformational leadership. J Appl Psychol 94(1):77–89. https://doi.org/10.1037/a0013077

Körner M (2010) Interprofessional teamwork in medical rehabilitation: a comparison of multidisciplinary and interdisciplinary team approach. Clin Rehabil 24(8):745–755. https://doi.org/10.1177/0269215510367538

Kurtzberg TR (2005) Feeling creative, being creative: an empirical study of diversity and creativity in teams. Creat Res J 17(1):51–65. https://doi.org/10.1207/s15326934crj1701_5

Kwiek M, Roszka W (2022) Academic vs. biological age in research on academic careers: a large-scale study with implications for scientifically developing systems. Scientometrics 127(6):3543–3575. https://doi.org/10.1007/s11192-022-04363-0

Leahey E, Barringer SN, Ring-Ramirez M (2019) Universities’ structural commitment to interdisciplinary research. Scientometrics 118(3):891–919. https://doi.org/10.1007/s11192-018-2992-3

Li H, Yin Z (2023) Influence of publication on university ranking: citation, collaboration, and level of interdisciplinary research. J Librariansh Inf Sci 55(3):828–835. https://doi.org/10.1177/09610006221106178

Liang W, Gu J, Nyland C (2022) China’s new research evaluation policy: evidence from economics faculty of Elite Chinese universities. Res Policy 51(1):104407. https://doi.org/10.1016/j.respol.2021.104407

Lim J, Richardson JC (2022) Considering how disciplinary differences matter for successful online learning through the Community of Inquiry lens. Comput Educ 187:104551. https://doi.org/10.1016/j.compedu.2022.104551

Lindblom-Ylänne S, Nevgi A, Trigwell K (2011) Regulation of university teaching. Instr Sci 39(4):483–495. https://doi.org/10.1007/s11251-010-9141-6

Long RG, Bowers WP, Barnett T, White MC (1998) Research productivity of graduates in management: effects of academic origin and academic affiliation. Acad Manag J 41(6):704–714. https://doi.org/10.5465/256966

Lundine J, Bourgeault IL, Clark J, Heidari S, Balabanova D (2019) Gender bias in academia. Lancet 393(10173):741–743. https://doi.org/10.1016/S0140-6736(19)30281-8

Mannucci PV, Yong K (2018) The Differential Impact of Knowledge Depth and Knowledge Breadth on Creativity over Individual Careers. Acad Manage J 61(5):1741–1763. https://doi.org/10.5465/amj.2016.0529

Marbach-Ad G, Hunt C, Thompson KV (2019) Exploring the values undergraduate students attribute to cross-disciplinary skills needed for the workplace: an analysis of five STEM disciplines. J Sci Educ Technol 28(5):452–469. https://doi.org/10.1007/s10956-019-09778-8

Mazzoleni S, Russo L, Giannino F, Toraldo G, Siettos C (2021) Mathematical modelling and numerical bifurcation analysis of inbreeding and interdisciplinarity dynamics in academia. J Comput Appl Math 385:113194. https://doi.org/10.1016/j.cam.2020.113194

Article   MathSciNet   Google Scholar  

Mcdossi O (2022) Epistemological similarities, prestige hierarchies, and double major combinations. High Educ Res Dev 41(3):820–834. https://doi.org/10.1080/07294360.2021.1877627

Millar MM (2013) Interdisciplinary research and the early career: the effect of interdisciplinary dissertation research on career placement and publication productivity of doctoral graduates in the sciences. Res Policy 42(5):1152–1164. https://doi.org/10.1016/j.respol.2013.02.004

Article   ADS   Google Scholar  

Morgan AC, LaBerge N, Larremore DB, Galesic M, Brand JE, Clauset A (2022) Socioeconomic roots of academic faculty. Nat Hum Behav 6(12):12. https://doi.org/10.1038/s41562-022-01425-4

Morichika N, Shibayama S (2015) Impact of inbreeding on scientific productivity: A case study of a Japanese university department. Res Eval 24(2):146–157. https://doi.org/10.1093/reseval/rvv002

Muffo JA, Langston IW (1981) Biglan’s dimensions: are the perceptions empirically based? Res High Educ 15(2):141–159. https://doi.org/10.1007/BF00979594

Nagle F, Teodoridis F (2020) Jack of all trades and master of knowledge: the role of diversification in new distant knowledge integration. Strat Manag J 41(1):55–85. https://doi.org/10.1002/smj.3091

Nandan M, London M (2013) Interdisciplinary professional education: training college students for collaborative social change. Educ + Train 55(8/9):815–835. https://doi.org/10.1108/ET-06-2013-0078

O’Donovan BM (2019) Patchwork quilt or woven cloth? The student experience of coping with assessment across disciplines. Stud High Educ 44(9):1579–1590. https://doi.org/10.1080/03075079.2018.1456518

Odugbesan JA, Aghazadeh S, Al Qaralleh RE, Sogeke OS (2023) Green talent management and employees’ innovative work behavior: the roles of artificial intelligence and transformational leadership. J Knowl Manag 27(3):696–716. https://doi.org/10.1108/JKM-08-2021-0601

Paulsen MB, Wells CT (1998) Domain differences in the epistemological beliefs of college students. Res High Educ 39(4):365–384. https://doi.org/10.1023/A:1018785219220

Petersen AM, Ahmed ME, Pavlidis I (2021) Grand challenges and emergent modes of convergence science. Hum Soc Sci Commun 8(1):1. https://doi.org/10.1057/s41599-021-00869-9

Rhoten D, Pfirman S (2007) Women in interdisciplinary science: exploring preferences and consequences. Res Policy 36(1):56–75. https://doi.org/10.1016/j.respol.2006.08.001

Rosman T, Seifried E, Merk S (2020) Combining intra- and interindividual approaches in epistemic beliefs research. Front Psychol 11. https://www.frontiersin.org/articles/10.3389/fpsyg.2020.00570

Ryazanova O, Jaskiene J (2022) Managing individual research productivity in academic organizations: a review of the evidence and a path forward. Res Policy 51(2):104448. https://doi.org/10.1016/j.respol.2021.104448

Ryazanova O, McNamara P (2019) Choices and consequences: impact of mobility on research-career capital and promotion in business schools. Acad Manag Learn Educ 18(2):186–212. https://doi.org/10.5465/amle.2017.0389

Scalf P, Torralbo A, Tapia E, Beck D (2013) Competition explains limited attention and perceptual resources: implications for perceptual load and dilution theories. Front Psychol 4. https://www.frontiersin.org/articles/10.3389/fpsyg.2013.00243

Sherman MG, Tookes HE (2022) Female representation in the academic finance profession. J Financ 77(1):317–365. https://doi.org/10.1111/jofi.13094

Simpson A (2017) The surprising persistence of Biglanas classification scheme. Stud High Educ 42(8):1520–1531. https://doi.org/10.1080/03075079.2015.1111323

Smart JC, Elton CF (1982) Validation of the Biglan model. Res High Educ 17(3):213–229. https://doi.org/10.1007/BF00976699

Smeets V, Warzynski F, Coupé T (2006) Does the academic labor market initially allocate new graduates efficiently? J Econ Perspect 20(3):161–172. https://doi.org/10.1257/jep.20.3.161

Staupe-Delgado R, Abdel-Fattah D, Pursiainen C (2022) A discipline without a name? Contrasting three fields dealing with hazards and disaster. Int J Disaster Risk Reduct 70:102751. https://doi.org/10.1016/j.ijdrr.2021.102751

Stoecker JL (1993) The Biglan classification revisited. Res High Educ 34(4):451–464. https://doi.org/10.1007/BF00991854

Tseng Y-W, Rowe F, Lin ES (2023) An Elon Musk generalist or a specialist? The impacts of interdisciplinary learning on post-graduation outcomes. Stud High Educ 0(0):1–26. https://doi.org/10.1080/03075079.2023.2252889

Waltman L (2016) A review of the literature on citation impact indicators. J Informetr 10(2):365–391. https://doi.org/10.1016/j.joi.2016.02.007

Wang C, Chin T, Lin J-H (2020) Openness and firm innovation performance: the moderating effect of ambidextrous knowledge search strategy. J Knowl Manag 24(2):301–323. https://doi.org/10.1108/JKM-04-2019-0198

Weng L, Flammini A, Vespignani A, Menczer F (2012) Competition among memes in a world with limited attention. Sci Rep 2(1):1. https://doi.org/10.1038/srep00335

Wiggins A, Sawyer S (2012) Intellectual diversity and the faculty composition of iSchools. J Am Soc Inf Sci Technol 63(1):8–21. https://doi.org/10.1002/asi.21619

Wright CB, Vanderford NL (2017) What faculty hiring committees want. Nat Biotechnol 35(9):9. https://doi.org/10.1038/nbt.3962

Xiao T, Makhija M, Karim S (2022) A knowledge recombination perspective of innovation: review and new research directions. J Manag 48(6):1724–1777. https://doi.org/10.1177/01492063211055982

Yang J, Wu Q, Wang C (2022) Research networks and the initial placement of PhD holders in academia: evidence from social science fields. Scientometrics 127(6):3253–3278. https://doi.org/10.1007/s11192-022-04394-7

Zadravec KA, Kočar S (2023) The impact of academic disciplines on a constructively aligned internationalised curriculum. Higher Educ. https://doi.org/10.1007/s10734-023-01008-w

Zahra SA, Newey LR (2009) Maximizing the impact of organization science: theory-building at the intersection of disciplines and/or fields. J Manag Stud 46(6):1059–1075. https://doi.org/10.1111/j.1467-6486.2009.00848.x

Zheng X, Zhou W, Ni C, Wang C (2022) The influencing mechanism of research training on Chinese STEM Ph.D. students’ career interests. Asia Pac Educ Rev. https://doi.org/10.1007/s12564-022-09775-4

Zhu Y, Yan E (2017) Examining academic ranking and inequality in library and information science through faculty hiring networks. J Informetr 11(2):641–654. https://doi.org/10.1016/j.joi.2017.04.007

Download references

Acknowledgements

We gratefully acknowledge the support of the National Science Foundation of China (72374023) and the Ministry of Science and Technology, PRC (QN2022178002L).

Author information

Authors and affiliations.

Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA

Wenjing Lyu

School of Humanities and Social Sciences, Beijing Institute of Technology, Beijing, China

Yuanhao Huang & Jin Liu

You can also search for this author in PubMed   Google Scholar

Contributions

WL and YH jointly designed the research, performed the analysis, and were actively involved in writing and revising the manuscript. And WL played a key role in conceptualizing the study and validating the findings. JL contributed by sharing essential data and reviewing the main results. All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

Corresponding author

Correspondence to Jin Liu .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Informed consent

Additional information.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Lyu, W., Huang, Y. & Liu, J. The multifaceted influence of multidisciplinary background on placement and academic progression of faculty. Humanit Soc Sci Commun 11 , 350 (2024). https://doi.org/10.1057/s41599-024-02818-8

Download citation

Received : 07 October 2023

Accepted : 08 February 2024

Published : 01 March 2024

DOI : https://doi.org/10.1057/s41599-024-02818-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

An adaptable and personalized framework for top-n course recommendations in online learning.

• Samina Amin
• M. Irfan Uddin
• Hamdan Ahmed Alzahrani

Scientific Reports (2024)

Genetically optimized neural network for college English teaching evaluation method

Education and Information Technologies (2024)

Research on Optimizing 6G Data-Driven Decision-Making Using Wireless Cyber Enterprise Innovation Process Using Decision Tree Algorithm

• Caishuang Hu
• Yizhou Zhao

Wireless Personal Communications (2024)

Deep Learning-Based Relation Extraction Model for Chinese Medical Case in 6G Cyber Physical Model

• Jinyang Zhu
• Oingyue Gong

The Application of 6G and Augmented Reality Technology in Education and Training

• Yangloucai Zhang
• Muhammad Farid Azizul Bin Azizui

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Theoretical quantum speedup with the quantum approximate optimization algorithm

In a new paper in Science Advances on May 29, researchers at JPMorgan Chase, the U.S. Department of Energy's (DOE) Argonne National Laboratory and Quantinuum have demonstrated clear evidence of a quantum algorithmic speedup for the quantum approximate optimization algorithm (QAOA).

This algorithm has been studied extensively and has been implemented on many quantum computers. It has potential application in fields such as logistics, telecommunications, financial modeling and materials science.

"This work is a significant step towards reaching quantum advantage, laying the foundation for future impact in production," said Marco Pistoia, head of Global Technology Applied Research at JPMorgan Chase.

The team examined whether a quantum algorithm with low implementation costs could provide a quantum speedup over the best-known classical methods. QAOA was applied to the Low Autocorrelation Binary Sequences problem, which has significance in understanding the behavior of physical systems, signal processing and cryptography. The study showed that if the algorithm was asked to tackle increasingly larger problems, the time it would take to solve them would grow at a slower rate than that of a classical solver.

To explore the quantum algorithm's performance in an ideal noiseless setting, JPMorgan Chase and Argonne jointly developed a simulator to evaluate the algorithm's performance at scale. It was built on the Polaris supercomputer, accessed through the Argonne Leadership Computing Facility (ALCF), a DOE Office of Science user facility. The ALCF is supported by DOE's Advanced Scientific Computing Research program.

"The large-scale quantum circuit simulations efficiently utilized the DOE petascale supercomputer Polaris located at the ALCF. These results show how high performance computing can complement and advance the field of quantum information science," said Yuri Alexeev, a computational scientist at Argonne. Jeffrey Larson, a computational mathematician in Argonne's Mathematics and Computer Science Division, also contributed to this research.

To take the first step toward practical realization of the speedup in the algorithm, the researchers demonstrated a small-scale implementation on Quantinuum's System Model H1 and H2 trapped-ion quantum computers. Using algorithm-specific error detection, the team reduced the impact of errors on algorithmic performance by up to 65%.

"Our long-standing partnership with JPMorgan Chase led to this meaningful and noteworthy three-way research experiment that also brought in Argonne. The results could not have been achieved without the unprecedented and world leading quality of our H-Series Quantum Computer, which provides a flexible device for executing error-correcting and error-detecting experiments on top of gate fidelities that are years ahead of other quantum computers," said Ilyas Khan, founder and chief product officer of Quantinuum.

• Quantum Physics
• Quantum Computing
• Spintronics
• Computers and Internet
• Quantum Computers
• Computer Programming
• Spintronics Research
• Quantum number
• Quantum computer
• Quantum entanglement
• Quantum dot
• Absolute zero
• Quantum tunnelling
• Electron configuration
• Linus Pauling

Story Source:

Materials provided by DOE/Argonne National Laboratory . Note: Content may be edited for style and length.

Journal Reference :

• Ruslan Shaydulin, Changhao Li, Shouvanik Chakrabarti, Matthew DeCross, Dylan Herman, Niraj Kumar, Jeffrey Larson, Danylo Lykov, Pierre Minssen, Yue Sun, Yuri Alexeev, Joan M. Dreiling, John P. Gaebler, Thomas M. Gatterman, Justin A. Gerber, Kevin Gilmore, Dan Gresh, Nathan Hewitt, Chandler V. Horst, Shaohan Hu, Jacob Johansen, Mitchell Matheny, Tanner Mengle, Michael Mills, Steven A. Moses, Brian Neyenhuis, Peter Siegfried, Romina Yalovetzky, Marco Pistoia. Evidence of scaling advantage for the quantum approximate optimization algorithm on a classically intractable problem . Science Advances , 2024; 10 (22) DOI: 10.1126/sciadv.adm6761

Cite This Page :

Explore More

• Resting Brain: Neurons Rehearse for Future
• Observing Single Molecules
• A Greener, More Effective Way to Kill Termites
• One Bright Spot Among Melting Glaciers
• Martian Meteorites Inform Red Planet's Structure
• Volcanic Events On Jupiter's Moon Io: High Res
• What Negative Adjectives Mean to Your Brain
• 'Living Bioelectronics' Can Sense and Heal Skin
• Extinct Saber-Toothed Cat On Texas Coast
• Some Black Holes Survive in Globular Clusters

Trending Topics

Strange & offbeat.

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

• Publications
• Our Methods
• Short Reads
• Tools & Resources

Read Our Research On:

Public’s Positive Economic Ratings Slip; Inflation Still Widely Viewed as Major Problem

1. views of the nation’s economy, table of contents.

• Views of top problems facing the nation
• Americans’ views of the state of the nation
• Similar shares in both parties view personal financial situation positively
• Americans’ views on the future of the economy and their financial situation
• Changes in views of the country’s top problems
• Acknowledgments
• The American Trends Panel survey methodology

Fewer than a quarter of Americans (23%) currently rate the country’s economic conditions as excellent or good, while 36% say they are poor and about four-in-ten (41%) view conditions as “only fair.”

While positive ratings of the economy have slowly climbed since the summer of 2022, there has been a slight drop  since the start of the year – when 28% rated economic conditions as excellent or good.

This change has been largely driven by Democrats and Democratic leaners: In January of this year, 44% of Democrats rated the economy positively, compared with 37% now.

Still, ratings among Democrats remain higher than they were last year.

Views among Republicans and GOP leaners remain negative: Just one-in-ten rate economic conditions as excellent or good, while half say they are poor and another four-in-ten view them as “only fair.”

Views of the nation’s economy have long been partisan.

• Republicans expressed far more positive views of the economy than did Democrats throughout most of Donald Trump’s presidency.
• Democrats have been consistently more likely than Republicans to rate conditions as excellent or good during Biden’s presidency. However, their ratings have been far less positive than Republicans’ ratings of the economy were when Trump was president. 

There also are wide differences in views of the economy by age and race and ethnicity – especially among Democrats.

Age, race and ethnicity

As in the past, Democrats under age 50 express much less positive views of the nation’s economy than do Democrats 50 and older:

• Just 21% of Democrats under 30 rate economic conditions positively, as do 29% of those 30 to 49.
• By contrast, nearly half of Democrats ages 50 to 64 (47%) and a majority of those 65 and older (55%) say conditions are excellent or good.

However, since January there has been a steeper decline in positive views among Democrats 65 and older (from 70% to 55%) than among Democrats in younger age groups.

Republicans are much less likely to view current economic conditions in positive terms across age groups.

There are also significant differences among Democrats by race and ethnicity. White Democrats are more likely than Black, Hispanic and Asian Democrats to rate the economy positively. However, ratings have dropped across these groups since January.

Views of personal finances and national economic ratings

As might be expected, those who rate their personal finances positively also are more likely to rate national economic conditions as excellent or good.

Among the roughly four-in-ten Americans (41%) who rate their own finances positively, 40% rate the national economy positively. Among those who say their finances are only fair or poor, far fewer say national economic conditions are excellent or good (14% among only fair, 6% among poor).

However, partisanship is a factor here as well. Among Democrats who have a positive evaluation of their finances, 58% rate economic conditions positively. That compares with just 19% of Republicans who give similarly positive ratings of their financial situation.

Overall, personal financial ratings have fluctuated less dramatically than national ratings.

However, the share of Americans who rate their personal finances as excellent or good declined from about 50% in 2021 to about 40% in 2022 and has remained at about that level since then.

About four-in-ten say their financial situation is in excellent or good shape (41%), while a similar share say their situation is in “only fair” shape (39%). Another 19% say their situation is in poor shape.

Americans’ ratings of their personal finances are considerably less partisan than their views of the nation’s economy. Roughly four-in-ten Democrats (44%) say their financial situation is in excellent or good shape.

When asked for their expectations of the country’s economic conditions a year from now, 43% of Americans say they expect it to be about the same as it currently is. About a quarter (24%) say they expect the economy will be better a year from now, and nearly a third (32%) expect conditions to worsen.

And when asked for their expectations of their own family’s financial situation a year from now, 49% of adults say they expect it to be about the same. Roughly a third (34%) say they expect their financial situation will be better a year from now, and 16% expect their situation to worsen.

The shares of the public who expect economic conditions to worsen on either a national level or personal level is smaller than in recent years .

Among partisans, similar shares expect economic conditions of the country to be better a year from now (23% of Republicans, 26% of Democrats). However, a larger share of Republicans than Democrats expect the country’s economic conditions to worsen (38% vs. 25%).

Republicans remain less hopeful than Democrats about the future of their personal financial situation. About three-in-ten Republicans (29%) say their family’s personal finances will be better a year from now, compared with 39% of Democrats who say the same. And twice as many Republicans as Democrats say they expect their own financial situation to worsen (22% vs. 11%).

Sign up for our weekly newsletter

Fresh data delivery Saturday mornings

Sign up for The Briefing

Weekly updates on the world of news & information

• Economic Conditions
• Issue Priorities
• National Conditions
• Partisanship & Issues
• Political Issues

Is College Worth It?

A look at small businesses in the u.s., state of the union 2024: where americans stand on the economy, immigration and other key issues, americans’ top policy priority for 2024: strengthening the economy, americans more upbeat on the economy; biden’s job rating remains very low, most popular, report materials.

1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

• Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

© 2024 Pew Research Center

Detection and Asynchronous Flow Prediction in a MOOC

• Original Research
• Published: 29 May 2024
• Volume 5 , article number  599 , ( 2024 )

Cite this article

• Sergio Iván Ramírez Luelmo   ORCID: orcid.org/0000-0002-7885-0123 1 ,
• Nour El Mawas   ORCID: orcid.org/0000-0002-0214-9840 2 ,
• Rémi Bachelet   ORCID: orcid.org/0000-0001-8725-0384 3 &
• Jean Heutte   ORCID: orcid.org/0000-0002-2646-3658 1  

10 Accesses

Explore all metrics

Flow is a human psychological state positively correlated to self-efficacy, motivation, engagement, and academic achievement. In a MOOC, flow detection and prediction would potentially allow for learners’ content personalization, fostering engagement and increasing already-low completion rates. In this study, we propose a Machine Learning flow-predicting model by pairing the results of the EduFlow-2 and Flow-Q measure instruments issued to participants of a MOOC ( n  = 1589, 2-year data collection). The resulting flow-predicting-model detects flow in an automatic, asynchronous fashion by applying only the EduFlow-2 measurement instrument. Our model proposal predicts flow presence with greater precision than it detects flow absence.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

https://mooc.gestiondeprojet.pm/

http://bit.ly/2LozEFI

https://nextcloud.univ-lille.fr/index.php/s/EH4XpjSn2N4kw8Y

https://scikit-learn.org/

https://www.python.org/

https://colab.research.google.com/

https://codecarbon.io/

https://joblib.readthedocs.io/

Yousef AMF, Chatti MA, Schroeder U, Wosnitza M, Jakobs H. MOOCs—a review of the state-of-the-Art. In: Proceedings of the 6th international conference on computer supported education. Barcelona, Spain: SCITEPRESS; 2014. p. 9–20. https://doi.org/10.5220/0004791400090020.

Amruta A, Ramgir VN. Adoption of open learning systems and MOOCS during COVID-19 by academic libraries. Int J Libr Inform Studi. 2021;2021(11):56–64.

Google Scholar  

Kichu M, Bhattacharya M. COVID-19 pandemic impels surge in MOOC learning and the new normal: a literature review. Int J Innov Res Technol. 2021;7:282–5. https://doi.org/10.6084/m9.figshare.14350622 .

Article   Google Scholar  

Shah D. By the numbers: MOOCs in 2020. The report by class central. 2020.

Shah D. The second year of the MOOC: a review of MOOC stats and trends in 2020. The Report by Class Central; 2020.

Xiong Y, Ling Q, Li X. Ubiquitous e-teaching and e-learning: China’s massive adoption of online education and launching MOOCs internationally during the COVID-19 outbreak. Wirel Commun Mobile Comput. 2021. https://doi.org/10.1155/2021/6358976 .

Jordan K. Initial trends in enrolment and completion of massive open online courses. Int Rev Res Open Distrib Learn. 2014. https://doi.org/10.19173/irrodl.v15i1.1651 .

Yuan L, Powell SJ. MOOCs and open education: implications for higher education. Report Cetis; 2013.

Jung Y, Lee J. Learning engagement and persistence in massive open online courses (MOOCS). Comput Educ. 2018;122:9–22. https://doi.org/10.1016/j.compedu.2018.02.013 .

Turner JC, Patrick H. How does motivation develop and why does it change? Reframing motivation research. Educ Psychol. 2008;43:119–31. https://doi.org/10.1080/00461520802178441 .

Wang Y, Baker R. Grit and intention: Why do learners complete MOOCs? Int Rev Res Open Distribu Learn. 2018. https://doi.org/10.19173/irrodl.v19i3.3393 .

Watted A, Barak M. Motivating factors of MOOC completers: comparing between university-affiliated students and general participants. Int High Edu. 2018;37:11–20. https://doi.org/10.1016/j.iheduc.2017.12.001 .

EFRN. What is flow? European flow researchers network; 2014.

Rufi S, Javaloy F, Batista-Foguet JM, Solanas A, Páez D. Flow dimensions on daily activities with the Spanish version of the flow scale (DFS). Span J Psychol. 2014;17:1–11. https://doi.org/10.1017/sjp.2014.34 .

Chen M, Wang X, Wang J, Zuo C, Tian J, Cui Y. Factors affecting college students’ continuous intention to use online course platform. SN Comput Sci. 2021;2:114. https://doi.org/10.1007/s42979-021-00498-8 .

El Mawas N, Gilliot J-M, Garlatti S, Euler R, Pascual S. As one size doesn’t fit all, personalized massive open online courses are required. In: McLaren BM, Reilly R, Zvacek S, Uhomoibhi J, editors. Computer supported education, vol. 1022. Communications in computer and information science. Cham: Springer; 2019. p. 470–88. https://doi.org/10.1007/978-3-030-21151-6_22 .

Chapter   Google Scholar  

Sunar AS, Abdullah NA, White S, Davis HC. Personalisation of MOOCs: the state of the art. In: Proceedings of the 7th international conference on computer supported education, vol. 1 CSEDU. SCITEPRESS; 2015. p. 88–97. https://doi.org/10.5220/0005445200880097 .

El Mawas N, Heutte J. A flow measurement instrument to test the students’ motivation in a computer science course. In: CSEDU 2019–Proceedings of the 11th international conference on computer supported education, vol. 1 2019. p. 495–505. hal.archives-ouvertes.fr. https://doi.org/10.5220/0007771504950505 .

Csíkszentmihályi MR. Flow and the foundations of positive psychology. The collected works of Mihaly Csikszentmihalyi. New York: Springer; 2014. https://doi.org/10.1007/978-94-017-9088-8_14 .

Book   Google Scholar  

Rheinberg F, Engeser S. Intrinsic motivation and flow. In: Heckhausen J, Heckhausen H, editors. Motivation and action. Cham: Springer; 2018. p. 579–622.

Raschka S, Mirjalili V. Python machine learning: machine learning and deep learning with Python, scikit-learn, and TensorFlow 2. 3rd ed. Expert Insight. Birmingham Mumbai: Packt; 2019.

Conati C, Porayska-Pomsta K, Mavrikis M. AI in education needs interpretable machine learning: lessons from open learner modelling; 2018. arXiv:1807.00154 [cs].

Moneta GB, Csíkszentmihályi MR. The effect of perceived challenges and skills on the quality of subjective experience. J Person. 1996;64:275–310. https://doi.org/10.1111/j.1467-6494.1996.tb00512.x .

Pfister R. Flow im Alltag: Untersuchungen zum Quadrantenmodell des Flow-Erlebens und zum Konzept der autotelischen Persönlichkeit mit der experience sampling method (ESM). Peter Lang; 2002.

Di Mitri D, Scheffel M, Drachsler H, Börner D, Ternier S, Specht M. Learning pulse: a machine learning approach for predicting performance in self-regulated learning using multimodal data. In: Proceedings of the seventh international learning analytics & knowledge conference; 2017. p. 188–97. https://doi.org/10.1145/3027385.3027447 .

Hussain S, Monkaresi H, Calvo RA. Categorical vs. dimensional representations in multimodal affect detection during learning. In: Cerri SA, Clancey WJ, Papadourakis G, Panourgia K, editors. Intelligent tutoring systems lecture notes in computer science. Berlin: Springer; 2012. p. 78–83. https://doi.org/10.1007/978-3-642-30950-2_11 .

Csíkszentmihályi MR. Beyond boredom and anxiety: the experience of play in work and games. 1st ed. San Francisco: Jossey Press; 1975.

Csíkszentmihályi MR, Csíkszentmihályi IS, editors. Optimal experience: psychological studies of flow in consciousness. First. Optimal experience: psychological studies of flow in consciousness. New York: Cambridge University Press; 1988.

Csíkszentmihályi MR. Flow: the psychology of optimal experience. London: Harper Perennial; 1990.

Heutte J. Les fondements de l’éducation positive: perspective psychosociale et systémique de l’apprentissage. Éducation Sup. Malakoff: Dunod; 2019.

Jackson SA, Marsh HW. Development and validation of a scale to measure optimal experience: the flow state scale. J Sport Exer Psychol. 1996;18:17–35. https://doi.org/10.1123/jsep.18.1.17 .

Peifer C, Wolters G, Harmat’ L, Heutte J, Tan J, Freire T, Tavares D, et al. A scoping review of flow research. Front Psychol. 2022. https://doi.org/10.3389/fpsyg.2022.815665 .

Deci EL. Intrinsic motivation. First. Perspectives in social psychology. New York: Plenum Press; 1975. https://doi.org/10.1007/978-1-4613-4446-9 .

White RW. Motivation reconsidered: the concept of competence. Psychol Rev. 1959;66:297–333. https://doi.org/10.1037/h0040934 .

Abyaa A, Idrissi MK, Bennani S. Learner modelling: systematic review of the literature from the last 5 years. Educ Tech Res Dev. 2019;67:1105–43. https://doi.org/10.1007/s11423-018-09644-1 .

Efklides A, Volet S, editors. Feelings and emotions in the learning process, vol. 15. London: Elsevier; 2005.

Medina-Medina N, García-Cabrera L. A taxonomy for user models in adaptive systems: special considerations for learning environments. Knowl Eng Rev. 2016;31:124–41. https://doi.org/10.1017/S0269888916000035 .

Csíkszentmihályi MR, Abuhamdeh S, Nakamura J. Flow. In: Elliot AJ, Dweck CS, editors. Handbook of competence and motivation. London: The Guilford Press; 2005. p. 598–608.

Motlagh SE, Amrai K, Yazdani MJ, Abderahim HA, Souri H. The relationship between self-efficacy and academic achievement in high school students. Proc Soc Behav Sci. 2011;15:765–8. https://doi.org/10.1016/j.sbspro.2011.03.180 .

Skadberg YX, Kimmel JR. Visitors’ flow experience while browsing a Web site: its measurement, contributing factors and consequences. Comput Hum Behav. 2004;20:403–22. https://doi.org/10.1016/S0747-5632(03)00050-5 .

Mayers PL. Flow in adolescence and its relation to school experience. Unpublished doctoral dissertation, University of Chicago; 1978.

Jackson SA, Eklund RC. Assessing flow in physical activity: the flow state scale-2 and dispositional flow scale-2. J Sport Exerc Psychol. 2002;24:133–50. https://doi.org/10.1123/jsep.24.2.133 .

Jackson SA, Martin A, Eklund RC. Long and short measures of flow: the construct validity of the FSS-2, DFS-2, and new brief counterparts. J Sport Exerc Psychol. 2008;30:561–87. https://doi.org/10.1123/jsep.30.5.561 .

Rheinberg F, Vollmeyer R, Engeser S. Kapitel 14 die Erfassung des flow-Erlebens. In: Stiensmeier-Pelster J, Rheinberg F, editors. Diagnostik von motivation und Selbstkonzept, vol. 2. Göttingen: Hogrefe Verlag GmbH & Company KG; 2003.

Ghani JA, Deshpande SP. Task characteristics and the experience of optimal flow in human–computer interaction. J Psychol. 1994;128:381–91. https://doi.org/10.1080/00223980.1994.9712742 .

Heutte J, Fenouillet F, Kaplan J, Martin-Krumm C, Bachelet R. Chapter 9 The EduFlow model: a contribution toward the study of optimal learning environments. In: Flow experience. London: Springer; 2016. p. 127–43. https://doi.org/10.1007/978-3-319-28634-1_9 .

Heutte J, Fenouillet F, Martin-Krumm C, Boniwell I, Csíkszentmihályi MR. Proposal for a conceptual evolution of the flow in education (EduFlow) model. In: 8th European conference on positive psychology (ECPP 2016). Angers, France; 2016.

Heutte J, Fenouillet F, Martin-Krumm C, Gute G, Raes A, Gute D, Bachelet R, Csíkszentmihályi MR. Optimal experience in adult learning: conception and validation of the flow in education scale (EduFlow-2). Front Psychol. 2021. https://doi.org/10.3389/fpsyg.2021.828027 .

Larson R, Csíkszentmihályi MR. The experience sampling method. In: Csíkszentmihályi MR, editor. Flow and the foundations of positive psychology. Springer: Dordrecht; 2014. p. 21–34. https://doi.org/10.1007/978-94-017-9088-8_2 .

Moneta GB. On the conceptualization and measurement of flow. In: Peifer C, Engeser S, editors. Advances in flow research. Cham: Springer; 2021. p. 31–69. https://doi.org/10.1007/978-3-030-53468-4_2 .

Nakamura J, Csíkszentmihályi MR. Chapter 18 Flow theory and research. In: Nakamura J, Csíkszentmihályi MR, Lopez SJ, Snyder CR, editors. The Oxford handbook of positive psychology. 2nd ed. New York: Oxford University Press; 2009. p. 194–206. https://doi.org/10.1093/oxfordhb/9780195187243.013.0018 .

Cheron G. How to measure the psychological “Flow”? A neuroscience perspective. Front Psychol. 2016. https://doi.org/10.3389/fpsyg.2016.01823 .

Obadă D-R. Pretesting flow questionnaire design using eye-tracking: an exploratory study. In: Argumentum. J Seminar of Discursive Logic, Argumentation theory and rhetoric. vol. 1; 2021.

Peifer C. Psychophysiological correlates of flow-experience. In: Engeser S, editor. Advances in flow research. New York: Springer; 2012. p. 139–64.

Hoffman DL, Novak TP. Flow online: lessons learned and future prospects. J Interact Mark. 2009;23:23–34. https://doi.org/10.1016/J.INTMAR.2008.10.003 .

de Moura Jr PJ, Bellini CGP. The measurement of flow and social flow at work: a 30-year systematic review of the literature. Pers Rev. 2019;49:537–70. https://doi.org/10.1108/PR-07-2018-0240 .

Asakawa K. Flow experience, culture, and well-being: How do autotelic Japanese college students feel, behave, and think in their daily lives? J Happiness Stud. 2010;11:205–23. https://doi.org/10.1007/s10902-008-9132-3 .

Bassi M, Fave AD. Optimal experience among teachers: new insights into the work paradox. J Psychol. 2012;146:533–57. https://doi.org/10.1080/00223980.2012.656156 .

Bassi M, Steca P, Monzani D, Greco A, Fave AD. Personality and optimal experience in adolescence: implications for well-being and development. J Happiness Stud. 2014;15:829–43. https://doi.org/10.1007/s10902-013-9451-x .

Boffi M. Flow as a measure of political engagement. Moscow, Russia; 2012.

Delle Fave A, Massimini F. Optimal experience in work and leisure among teachers and physicians: individual and bio-cultural implications. Leis Stud. 2003;22:323–42. https://doi.org/10.1080/02614360310001594122 .

Jackman PC, Crust L, Swann C. Systematically comparing methods used to study flow in sport: a longitudinal multiple-case study. Psychol Sport Exercise. 2017;32:113–23. https://doi.org/10.1016/j.psychsport.2017.06.009 .

Johnson JA, Keiser HN, Skarin EM, Ross SR. The dispositional flow scale-2 as a measure of autotelic personality: an examination of criterion-related validity. J Personal Assess. 2014;96:465–70. https://doi.org/10.1080/00223891.2014.891524 .

Mikicin M. Relationships between experiencing flow state and personality traits, locus of control and achievement motivation in swimmers. Wych Fiz I Sport. 2007;51:323.

Moneta GB. Opportunity for creativity in the job as a moderator of the relation between trait intrinsic motivation and flow in work. Motiv Emot. 2012;36:491–503. https://doi.org/10.1007/s11031-012-9278-5 .

Peifer C, Engeser S. Theoretical integration and future lines of flow research. In: Peifer C, Engeser S, editors. Advances in flow research. Cham: Springer; 2021. p. 417–39. https://doi.org/10.1007/978-3-030-53468-4_16 .

Redaelli C, Riva G. Flow for presence questionnaire. In: Canetta L, Redaelli C, Flores M, editors. Digital factory for human-oriented production systems. 1st ed. London: Springer; 2011. p. 3–22.

Tse DCK, Nakamura J, Csíkszentmihályi MR. Flow experiences across adulthood: preliminary findings on the continuity hypothesis. J Happiness Stud. 2022;23:1–24.

Wright JJ, Sadlo G, Stew G. Challenge-skills and mindfulness: an exploration of the conundrum of flow process. OTJR Occup Particip Health. 2006;26:25–32. https://doi.org/10.1177/153944920602600104 .

Fave AD, Massimini F. Modernization and the changing contexts of flow in work and leisure. In: Csíkszentmihályi MR, Csíkszentmihályi IS, editors. Optimal experience: psychological studies of flow in consciousness. Cambridge: Cambridge University Press; 1988.

Parks BK. “Flow”, boredom, and anxiety in therapeutic work: a study of psychotherapists’ intrinsic motivation and professional development. Doctoral dissertation, Chicago, USA: University of Chicago; 1996.

Heutte J, Fenouillet F, Boniwell I, Martin-Krumm C, Csíkszentmihályi MR. Optimal learning experience in digital environments: theoretical concepts, measure and modelisation. In: Symposium “Digital learning in 21st century universities.” Atlanta, USA; 2014.

Heutte J. L’environnement optimal d’apprentissage vidéo-ludique : contribution de la psychologie positive à la définition d’une ingénierie ludo-éduquante autotélique. Séminaire presented at the CNAM‑ENJIM “bases cognitives, sociales et émotionnelles des jeux et médias interactifs numériques,” Angoûleme, France; 2015.

Subasi A. Machine learning techniques. In: Practical machine learning for data analysis using python. London: Elsevier; 2020. p. 91–202. https://doi.org/10.1016/B978-0-12-821379-7.00003-5 .

Isbell C, Littman ML, Norvig P. Software engineering of machine learning systems. Commun ACM. 2023;66:35–7. https://doi.org/10.1145/3539783 .

Dangeti P. Statistics for machine learning. London: Packt Publishing Ltd.; 2017.

The Royal Society. Explainable AI: the basics. DES6051. London, UK; 2019.

Ramírez Luelmo SI, El Mawas N, Bachelet R, Heutte J. Towards a machine learning flow-predicting model in a MOOC Context. In: Proceedings of the 14th international conference on computer supported education. SCITEPRESS; 2022. p. 124–34. https://doi.org/10.5220/0011070300003182 .

Chermann E. Enseignement en ligne: les 1001 secrets d’un MOOC qui cartonne. Le Monde, March 1, sec. Économie/Éducation; 2020.

Bachelet R. LE MOOC GdP: Chiffres presse. MOOC. MOOC Gestion de Projet; 2019.

Ferreira Marques J, Bernardino J. Analysis of data anonymization techniques. In: Proceedings of the 12th international joint conference on knowledge discovery, knowledge engineering and knowledge management. Budapest, Hungary: SCITEPRESS; 2020. p. 235–41. https://doi.org/10.5220/0010142302350241 .

Łucznik K, May J. Measuring individual and group flow in collaborative improvisational dance. Think Skills Creativity. 2021;40:100847. https://doi.org/10.1016/j.tsc.2021.100847 .

Delle Fave A, Massimini F, Bassi M. Psychological selection and optimal experience across cultures, vol. 2. Cross-cultural advancements in positive psychology. Dordrecht: Springer; 2011. https://doi.org/10.1007/978-90-481-9876-4 .

Allison MT, Duncan MC. Women, work, and leisure: the days of our lives. Leis Sci. 1987;9:143–61.

de Barba PG, Malekian D, Oliveira EA, Bailey J, Ryan T, Kennedy G. The importance and meaning of session behaviour in a MOOC. Comput Educ. 2020;146:103772. https://doi.org/10.1016/j.compedu.2019.103772 .

Lee Y. Effect of uninterrupted time-on-task on students’ success in massive open online courses (MOOCs). Comput Hum Behav. 2018;86:174–80. https://doi.org/10.1016/j.chb.2018.04.043 .

Ramírez Luelmo SI, El Mawas N, Heutte J. Towards open learner models including the flow state. In: Adjunct publication of the 28th ACM conference on user modeling, adaptation and personalization. Genoa, Italy: ACM; 2020. p. 305–10. https://doi.org/10.1145/3386392.3399295 .

Download references

This project was supported by the French government through the Programme Investissement d’Avenir (I-SITE ULNE/ANR-16-IDEX-0004 ULNE) managed by the Agence Nationale de la Recherche.

Author information

Authors and affiliations.

CIREL – Centre Interuniversitaire de Recherche en Éducation de Lille, Université de Lille, Campus Cité Scientifique, Bâtiments B5 – B6, Villeneuve d’Ascq, France

Sergio Iván Ramírez Luelmo & Jean Heutte

CREM – UR - Centre de recherche sur les médiations, Université de Lorraine, Île du Saulcy, Metz, France

Nour El Mawas

Centrale Lille, Université de Lille, Campus Cité Scientifique, Villeneuve d’Ascq, France

Rémi Bachelet

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Sergio Iván Ramírez Luelmo .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflict of interest. On behalf of all authors, the corresponding author states that there is no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Advances on Computer Supported Education” guest edited by James Uhomoibhi.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Ramírez Luelmo, S.I., El Mawas, N., Bachelet, R. et al. Detection and Asynchronous Flow Prediction in a MOOC. SN COMPUT. SCI. 5 , 599 (2024). https://doi.org/10.1007/s42979-024-02838-w

Download citation

Received : 13 September 2022

Accepted : 26 March 2024

Published : 29 May 2024

DOI : https://doi.org/10.1007/s42979-024-02838-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Autotelic experience
• Machine learning
• Logistic regression

Advertisement

• Find a journal
• Publish with us
• Track your research

• Asia Pacific
• Latin America
• Middle East & Africa
• North America
• Australia & New Zealand

Mainland China

• Hong Kong SAR, China
• Philippines
• Taiwan, China
• Channel Islands
• Netherlands
• Switzerland
• United Kingdom
• Saudi Arabia
• South Africa
• United Arab Emirates
• United States

From startups to legacy brands, you're making your mark. We're here to help.

• Innovation Economy Fueling the success of early-stage startups, venture-backed and high-growth companies.
• Midsize Businesses Keep your company growing with custom banking solutions for middle market businesses and specialized industries.
• Large Corporations Innovative banking solutions tailored to corporations and specialized industries.
• Commercial Real Estate Capitalize on opportunities and prepare for challenges throughout the real estate cycle.
• Community Impact Banking When our communities succeed, we all succeed. Local businesses, organizations and community institutions need capital, expertise and connections to thrive.
• International Banking Power your business' global growth and operations at every stage.
• Client Stories

Prepare for future growth with customized loan services, succession planning and capital for business equipment.

• Asset Based Lending Enhance your liquidity and gain the flexibility to capitalize on growth opportunities.
• Equipment Financing Maximize working capital with flexible equipment and technology financing.
• Trade & Working Capital Experience our market-leading supply chain finance solutions that help buyers and suppliers meet their working capital, risk mitigation and cash flow objectives.
• Syndicated Financing Leverage customized loan syndication services from a dedicated resource.
• Employee Stock Ownership Plans Plan for your business’s future—and your employees’ futures too—with objective advice and financing.

Institutional Investing

Serving the world's largest corporate clients and institutional investors, we support the entire investment cycle with market-leading research, analytics, execution and investor services.

• Institutional Investors We put our long-tenured investment teams on the line to earn the trust of institutional investors.
• Markets Direct access to market leading liquidity harnessed through world-class research, tools, data and analytics.
• Prime Services Helping hedge funds, asset managers and institutional investors meet the demands of a rapidly evolving market.
• Global Research Leveraging cutting-edge technology and innovative tools to bring clients industry-leading analysis and investment advice.
• Securities Services Helping institutional investors, traditional and alternative asset and fund managers, broker dealers and equity issuers meet the demands of changing markets.
• Financial Professionals
• Liquidity Investors

Providing investment banking solutions, including mergers and acquisitions, capital raising and risk management, for a broad range of corporations, institutions and governments.

• Center for Carbon Transition J.P. Morgan’s center of excellence that provides clients the data and firmwide expertise needed to navigate the challenges of transitioning to a low-carbon future.
• Corporate Finance Advisory Corporate Finance Advisory (“CFA”) is a global, multi-disciplinary solutions team specializing in structured M&A and capital markets. Learn more.
• Development Finance Institution Financing opportunities with anticipated development impact in emerging economies.
• Sustainable Solutions Offering ESG-related advisory and coordinating the firm's EMEA coverage of clients in emerging green economy sectors.
• Mergers and Acquisitions Bespoke M&A solutions on a global scale.
• Capital Markets Holistic coverage across capital markets.
• Capital Connect
• In Context Newsletter from J.P. Morgan
• Director Advisory Services

Accept Payments

Explore Blockchain

Client Service

Process Payments

Manage Funds

Safeguard Information

Banking-as-a-service

Send Payments

• Partner Network

A uniquely elevated private banking experience shaped around you.

• Banking We have extensive personal and business banking resources that are fine-tuned to your specific needs.
• Investing We deliver tailored investing guidance and access to unique investment opportunities from world-class specialists.
• Lending We take a strategic approach to lending, working with you to craft the fight financing solutions matched to your goals.
• Planning No matter where you are in your life, or how complex your needs might be, we’re ready to provide a tailored approach to helping your reach your goals.

Whether you want to invest on your own or work with an advisor to design a personalized investment strategy, we have opportunities for every investor.

• Invest on your own Unlimited $0 commission-free online stock, ETF and options trades with access to powerful tools to research, trade and manage your investments.
• Work with our advisors When you work with our advisors, you'll get a personalized financial strategy and investment portfolio built around your unique goals-backed by our industry-leading expertise.
• Expertise for Substantial Wealth Our Wealth Advisors & Wealth Partners leverage their experience and robust firm resources to deliver highly-personalized, comprehensive solutions across Banking, Lending, Investing, and Wealth Planning.
• Why Wealth Management?
• Retirement Calculators
• Market Commentary

Who We Serve

Explore a variety of insights.

• Global Research
• Newsletters

Insights by Topic

Explore a variety of insights organized by different topics.

Insights by Type

Explore a variety of insights organized by different types of content and media.  

• All Insights

We aim to be the most respected financial services firm in the world, serving corporations and individuals in more than 100 countries.

NEW YORK, NY; BROOMFIELD, CO; LEMONT, IL ; MAY 29, 2024 - In a new paper in Science Advances on May 29, researchers at JPMorgan Chase, the U.S. Department of Energy’s (DOE) Argonne National Laboratory and Quantinuum have demonstrated clear evidence of a quantum algorithmic speedup for the quantum approximate optimization algorithm ( QAOA ).

This algorithm has been studied extensively and has been implemented on many quantum computers. It has potential applications in fields such as logistics, telecommunications, financial modeling, and materials science.

“This work is a significant step towards reaching quantum advantage, laying the foundation for future impact in production,” says Marco Pistoia, Head of Global Technology Applied Research at JPMorgan Chase.

The team examined whether a quantum algorithm with low implementation costs could provide a quantum speedup over the best-known classical methods. QAOA was applied to the Low Autocorrelation Binary Sequences (LABS) problem, which has significance in understanding the behavior of physical systems, signal processing and cryptography. The study showed that if the algorithm was asked to tackle increasingly larger problems, the time it would take to solve them would grow at a slower rate than that of a classical solver.

To explore the quantum algorithm’s performance in an ideal noiseless setting, JPMorgan Chase and Argonne jointly developed a simulator to evaluate the algorithm’s performance at scale. It was built on the Polaris supercomputer, accessed through the Argonne Leadership Computing Facility (ALCF), a DOE Office of Science user facility. The ALCF is supported by DOE’s Advanced Scientific Computing Research program.

“The large-scale quantum circuit simulations efficiently utilized the DOE petascale supercomputer Polaris located at the ALCF. These results show how high-performance computing can complement and advance the field of quantum information science,” says Yuri Alexeev, a computational scientist at Argonne.

To take the first step toward practical realization of the speedup in the algorithm, the researchers demonstrated a small-scale implementation on Quantinuum’s System Model H1 and H2 trapped-ion quantum computers. Using algorithm-specific error detection, the team reduced the impact of errors on algorithmic performance by up to 65%.

“Our long-standing partnership with JPMorgan Chase led to this meaningful and noteworthy three-way research experiment that also brought in Argonne National Lab. The results could not have been achieved without the unprecedented and world leading quality of our H-Series Quantum Computer, which provides a flexible device for executing error-correcting and error-detecting experiments on top of gate fidelities that are years ahead of other quantum computers,” says Ilyas Khan, Founder and Chief Product Officer of Quantinuum.

Read the full research paper here.

About JPMorgan Chase JPMorgan Chase & Co. (NYSE: JPM) is a leading financial services firm based in the United States of America (“U.S.”), with operations worldwide. JPMorgan Chase had $4.1 trillion in assets and $337 billion in stockholders’ equity as of March 31, 2024. With over 63,000 technologists globally and an annual tech spend of $17 billion, JPMorgan Chase is dedicated to improving the design, analytics, development, coding, testing and application programming that goes into creating high quality software and new products. Under the J.P. Morgan and Chase brands, the Firm serves millions of customers in the U.S., and many of the world’s most prominent corporate, institutional and government clients globally. Visit http://www.jpmorganchase.com/tech for more information. 

About Argonne National Laboratory Argonne National Laboratory seeks solutions to pressing national problems in science and technology by conducting leading-edge basic and applied research in virtually every scientific discipline. Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science .

About Quantinuum Quantinuum ,  the world’s largest integrated quantum company, pioneers powerful quantum computers and advanced software solutions. Quantinuum’s technology drives breakthroughs in materials discovery, cybersecurity, and next-gen quantum AI. With almost 500 employees, including 370+ scientists and engineers, Quantinuum leads the quantum computing revolution across continents.

Quantinuum recently closed an equity fundraise anchored by JPMorgan Chase with additional participation from Mitsui & CO., Amgen and Honeywell, which remains the company’s majority shareholder, bringing the total capital raised by Quantinuum since inception to approximately $625 million.

The Honeywell trademark is used under license from Honeywell International Inc. Honeywell makes no representations or warranties with respect to this service.

You're now leaving J.P. Morgan

J.P. Morgan’s website and/or mobile terms, privacy and security policies don’t apply to the site or app you're about to visit. Please review its terms, privacy and security policies to see how they apply to you. J.P. Morgan isn’t responsible for (and doesn’t provide) any products, services or content at this third-party site or app, except for products and services that explicitly carry the J.P. Morgan name.

• Open access
• Published: 27 May 2024

Current status of community resources and priorities for weed genomics research

• Jacob Montgomery 1 ,
• Sarah Morran 1 ,
• Dana R. MacGregor   ORCID: orcid.org/0000-0003-0543-0408 2 ,
• J. Scott McElroy   ORCID: orcid.org/0000-0003-0331-3697 3 ,
• Paul Neve   ORCID: orcid.org/0000-0002-3136-5286 4 ,
• Célia Neto   ORCID: orcid.org/0000-0003-3256-5228 4 ,
• Martin M. Vila-Aiub   ORCID: orcid.org/0000-0003-2118-290X 5 ,
• Maria Victoria Sandoval 5 ,
• Analia I. Menéndez   ORCID: orcid.org/0000-0002-9681-0280 6 ,
• Julia M. Kreiner   ORCID: orcid.org/0000-0002-8593-1394 7 ,
• Longjiang Fan   ORCID: orcid.org/0000-0003-4846-0500 8 ,
• Ana L. Caicedo   ORCID: orcid.org/0000-0002-0378-6374 9 ,
• Peter J. Maughan 10 ,
• Bianca Assis Barbosa Martins 11 ,
• Jagoda Mika 11 ,
• Alberto Collavo 11 ,
• Aldo Merotto Jr.   ORCID: orcid.org/0000-0002-1581-0669 12 ,
• Nithya K. Subramanian   ORCID: orcid.org/0000-0002-1659-7396 13 ,
• Muthukumar V. Bagavathiannan   ORCID: orcid.org/0000-0002-1107-7148 13 ,
• Luan Cutti   ORCID: orcid.org/0000-0002-2867-7158 14 ,
• Md. Mazharul Islam 15 ,
• Bikram S. Gill   ORCID: orcid.org/0000-0003-4510-9459 16 ,
• Robert Cicchillo 17 ,
• Roger Gast 17 ,
• Neeta Soni   ORCID: orcid.org/0000-0002-4647-8355 17 ,
• Terry R. Wright   ORCID: orcid.org/0000-0002-3969-2812 18 ,
• Gina Zastrow-Hayes 18 ,
• Gregory May 18 ,
• Jenna M. Malone   ORCID: orcid.org/0000-0002-9637-2073 19 ,
• Deepmala Sehgal   ORCID: orcid.org/0000-0002-4141-1784 20 ,
• Shiv Shankhar Kaundun   ORCID: orcid.org/0000-0002-7249-2046 20 ,
• Richard P. Dale 20 ,
• Barend Juan Vorster   ORCID: orcid.org/0000-0003-3518-3508 21 ,
• Bodo Peters 11 ,
• Jens Lerchl   ORCID: orcid.org/0000-0002-9633-2653 22 ,
• Patrick J. Tranel   ORCID: orcid.org/0000-0003-0666-4564 23 ,
• Roland Beffa   ORCID: orcid.org/0000-0003-3109-388X 24 ,
• Alexandre Fournier-Level   ORCID: orcid.org/0000-0002-6047-7164 25 ,
• Mithila Jugulam   ORCID: orcid.org/0000-0003-2065-9067 15 ,
• Kevin Fengler 18 ,
• Victor Llaca   ORCID: orcid.org/0000-0003-4822-2924 18 ,
• Eric L. Patterson   ORCID: orcid.org/0000-0001-7111-6287 14 &
• Todd A. Gaines   ORCID: orcid.org/0000-0003-1485-7665 1  

Genome Biology volume  25 , Article number:  139 ( 2024 ) Cite this article

335 Accesses

1 Altmetric

Metrics details

Weeds are attractive models for basic and applied research due to their impacts on agricultural systems and capacity to swiftly adapt in response to anthropogenic selection pressures. Currently, a lack of genomic information precludes research to elucidate the genetic basis of rapid adaptation for important traits like herbicide resistance and stress tolerance and the effect of evolutionary mechanisms on wild populations. The International Weed Genomics Consortium is a collaborative group of scientists focused on developing genomic resources to impact research into sustainable, effective weed control methods and to provide insights about stress tolerance and adaptation to assist crop breeding.

Each year globally, agricultural producers and landscape managers spend billions of US dollars [ 1 , 2 ] and countless hours attempting to control weedy plants and reduce their adverse effects. These management methods range from low-tech (e.g., pulling plants from the soil by hand) to extremely high-tech (e.g., computer vision-controlled spraying of herbicides). Regardless of technology level, effective control methods serve as strong selection pressures on weedy plants and often result in rapid evolution of weed populations resistant to such methods [ 3 , 4 , 5 , 6 , 7 ]. Thus, humans and weeds have been locked in an arms race, where humans develop new or improved control methods and weeds adapt and evolve to circumvent such methods.

Applying genomics to weed science offers a unique opportunity to study rapid adaptation, epigenetic responses, and examples of evolutionary rescue of diverse weedy species in the face of widespread and powerful selective pressures. Furthermore, lessons learned from these studies may also help to develop more sustainable control methods and to improve crop breeding efforts in the face of our ever-changing climate. While other research fields have used genetics and genomics to uncover the basis of many biological traits [ 8 , 9 , 10 , 11 ] and to understand how ecological factors affect evolution [ 12 , 13 ], the field of weed science has lagged behind in the development of genomic tools essential for such studies [ 14 ]. As research in human and crop genetics pushes into the era of pangenomics (i.e., multiple chromosome scale genome assemblies for a single species [ 15 , 16 ]), publicly available genomic information is still lacking or severely limited for the majority of weed species. Recent reviews of current weed genomes identified 26 [ 17 ] and 32 weed species with sequenced genomes [ 18 ]—many assembled to a sub-chromosome level.

Here, we summarize the current state of weed genomics, highlighting cases where genomics approaches have successfully provided insights on topics such as population genetic dynamics, genome evolution, and the genetic basis of herbicide resistance, rapid adaptation, and crop dedomestication. These highlighted investigations all relied upon genomic resources that are relatively rare for weedy species. Throughout, we identify additional resources that would advance the field of weed science and enable further progress in weed genomics. We then introduce the International Weed Genomics Consortium (IWGC), an open collaboration among researchers, and describe current efforts to generate these additional resources.

Evolution of weediness: potential research utilizing weed genomics tools

Weeds can evolve from non-weed progenitors through wild colonization, crop de-domestication, or crop-wild hybridization [ 19 ]. Because the time span in which weeds have evolved is necessarily limited by the origins of agriculture, these non-weed relatives often still exist and can be leveraged through population genomic and comparative genomic approaches to identify the adaptive changes that have driven the evolution of weediness. The ability to rapidly adapt, persist, and spread in agroecosystems are defining features of weedy plants, leading many to advocate agricultural weeds as ideal candidates for studying rapid plant adaptation [ 20 , 21 , 22 , 23 ]. The insights gained from applying plant ecological approaches to the study of rapid weed adaptation will move us towards the ultimate goals of mitigating such adaptation and increasing the efficacy of crop breeding and biotechnology [ 14 ].

Biology and ecological genomics of weeds

The impressive community effort to create and maintain resources for Arabidopsis thaliana ecological genomics provides a motivating example for the emerging study of weed genomics [ 24 , 25 , 26 , 27 ]. Arabidopsis thaliana was the first flowering plant species to have its genome fully sequenced [ 28 ] and rapidly became a model organism for plant molecular biology. As weedy genomes become available, collection, maintenance, and resequencing of globally distributed accessions of these species will help to replicate the success found in ecological studies of A. thaliana [ 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. Evaluation of these accessions for traits of interest to produce large phenomics data sets (as in [ 36 , 37 , 38 , 39 , 40 ]) enables genome-wide association studies and population genomics analyses aimed at dissecting the genetic basis of variation in such traits [ 41 ]. Increasingly, these resources (e.g. the 1001 genomes project [ 29 ]) have enabled A. thaliana to be utilized as a model species to explore the eco-evolutionary basis of plant adaptation in a more realistic ecological context. Weedy species should supplement lessons in eco-evolutionary genomics learned from these experiments in A. thaliana .

Untargeted genomic approaches for understanding the evolutionary trajectories of populations and the genetic basis of traits as described above rely on the collection of genotypic information from across the genome of many individuals. While whole-genome resequencing accomplishes this requirement and requires no custom methodology, this approach provides more information than is necessary and is prohibitively expensive in species with large genomes. Development and optimization of genotype-by-sequencing methods for capturing reduced representations of newly sequence genomes like those described by [ 42 , 43 , 44 ] will reduce the cost and computational requirements of genetic mapping and population genetic experiments. Most major weed species do not currently have protocols for stable transformation, a key development in the popularity of A. thaliana as a model organism and a requirement for many functional genomic approaches. Functional validation of genes/variants believed to be responsible for traits of interest in weeds has thus far relied on transiently manipulating endogenous gene expression [ 45 , 46 ] or ectopic expression of a transgene in a model system [ 47 , 48 , 49 ]. While these methods have been successful, few weed species have well-studied viral vectors to adapt for use in virus induced gene silencing. Spray induced gene silencing is another potential option for functional investigation of candidate genes in weeds, but more research is needed to establish reliable delivery and gene knockdown [ 50 ]. Furthermore, traits with complex genetic architecture divergent between the researched and model species may not be amenable to functional genomic approaches using transgenesis techniques in model systems. Developing protocols for reduced representation sequencing, stable transformation, and gene editing/silencing in weeds will allow for more thorough characterization of candidate genetic variants underlying traits of interest.

Beyond rapid adaptation, some weedy species offer an opportunity to better understand co-evolution, like that between plants and pollinators and how their interaction leads to the spread of weedy alleles (Additional File 1 : Table S1). A suite of plant–insect traits has co-evolved to maximize the attraction of the insect pollinator community and the efficiency of pollen deposition between flowers ensuring fruit and seed production in many weeds [ 51 , 52 ]. Genetic mapping experiments have identified genes and genetic variants responsible for many floral traits affecting pollinator interaction including petal color [ 53 , 54 , 55 , 56 ], flower symmetry and size [ 57 , 58 , 59 ], and production of volatile organic compounds [ 60 , 61 , 62 ] and nectar [ 63 , 64 , 65 ]. While these studies reveal candidate genes for selection under co-evolution, herbicide resistance alleles may also have pleiotropic effects on the ecology of weeds [ 66 ], altering plant-pollinator interactions [ 67 ]. Discovery of genes and genetic variants involved in weed-pollinator interaction and their molecular and environmental control may create opportunities for better management of weeds with insect-mediated pollination. For example, if management can disrupt pollinator attraction/interaction with these weeds, the efficiency of reproduction may be reduced.

A more complete understanding of weed ecological genomics will undoubtedly elucidate many unresolved questions regarding the genetic basis of various aspects of weediness. For instance, when comparing populations of a species from agricultural and non-agricultural environments, is there evidence for contemporary evolution of weedy traits selected by agricultural management or were “natural” populations pre-adapted to agroecosystems? Where there is differentiation between weedy and natural populations, which traits are under selection and what is the genetic basis of variation in those traits? When comparing between weedy populations, is there evidence for parallel versus non-parallel evolution of weediness at the phenotypic and genotypic levels? Such studies may uncover fundamental truths about weediness. For example, is there a common phenotypic and/or genotypic basis for aspects of weediness among diverse weed species? The availability of characterized accessions and reference genomes for species of interest are required for such studies but only a few weedy species have these resources developed.

Population genomics

Weed species are certainly fierce competitors, able to outcompete crops and endemic species in their native environment, but they are also remarkable colonizers of perturbed habitats. Weeds achieve this through high fecundity, often producing tens of thousands of seeds per individual plant [ 68 , 69 , 70 ]. These large numbers in terms of demographic population size often combine with outcrossing reproduction to generate high levels of diversity with local effective population sizes in the hundreds of thousands [ 71 , 72 ]. This has two important consequences: weed populations retain standing genetic variation and generate many new mutations, supporting weed success in the face of harsh control. The generation of genomic tools to monitor weed populations at the molecular level is a game-changer to understanding weed dynamics and precisely testing the effect of artificial selection (i.e., management) and other evolutionary mechanisms on the genetic make-up of populations.

Population genomic data, without any environmental or phenotypic information, can be used to scan the genomes of weed and non-weed relatives to identify selective sweeps, pointing at loci supporting weed adaptation on micro- or macro-evolutionary scales. Two recent within-species examples include weedy rice, where population differentiation between weedy and domesticated populations was used to identify the genetic basis of weedy de-domestication [ 73 ], and common waterhemp, where consistent allelic differences among natural and agricultural collections resolved a complex set of agriculturally adaptive alleles [ 74 , 75 ]. A recent comparative population genomic study of weedy barnyardgrass and crop millet species has demonstrated how inter-specific investigations can resolve the signatures of crop and weed evolution [ 76 ] (also see [ 77 ] for a non-weed climate adaptation example). Multiple sequence alignments across numerous species provide complementary insight into adaptive convergence over deeper timescales, even with just one genomic sample per species (e.g., [ 78 , 79 ]). Thus, newly sequenced weed genomes combined with genomes available for closely related crops (outlined by [ 14 , 80 ]) and an effort to identify other non-weed wild relatives will be invaluable in characterizing the genetic architecture of weed adaptation and evolution across diverse species.

Weeds experience high levels of genetic selection, both artificial in response to agricultural practices and particularly herbicides, and natural in response to the environmental conditions they encounter [ 81 , 82 ]. Using genomic analysis to identify loci that are the targets of selection, whether natural or artificial, would point at vulnerabilities that could be leveraged against weeds to develop new and more sustainable management strategies [ 83 ]. This is a key motivation to develop genotype-by-environment association (GEA) and selective sweep scan approaches, which allow researchers to resolve the molecular basis of multi-dimensional adaptation [ 84 , 85 ]. GEA approaches, in particular, have been widely used on landscape-wide resequencing collections to determine the genetic basis of climate adaptation (e.g., [ 27 , 86 , 87 ]), but have yet to be fully exploited to diagnose the genetic basis of the various aspects of weediness [ 88 ]. Armed with data on environmental dimensions of agricultural settings, such as focal crop, soil quality, herbicide use, and climate, GEA approaches can help disentangle how discrete farming practices have influenced the evolution of weediness and resolve broader patterns of local adaptation across a weed’s range. Although non-weedy relatives are not technically required for GEA analyses, inclusion of environmental and genomic data from weed progenitors can further distinguish genetic variants underpinning weed origins from those involved in local adaptation.

New weeds emerge frequently [ 89 ], either through hybridization between species as documented for sea beet ( Beta vulgaris ssp. maritima) hybridizing with crop beet to produce progeny that are well adapted to agricultural conditions [ 90 , 91 , 92 ], or through the invasion of alien species that find a new range to colonize. Biosecurity measures are often in place to stop the introduction of new weeds; however, the vast scale of global agricultural commodity trade precludes the possibility of total control. Population genomic analysis is now able to measure gene flow between populations [ 74 , 93 , 94 , 95 ] and identify populations of origin for invasive species including weeds [ 96 , 97 , 98 ]. For example, the invasion route of the pest fruitfly Drosophila suzukii from Eastern Asia to North America and Europe through Hawaii was deciphered using Approximate Bayesian Computation on high-throughput sequencing data from a global sample of multiple populations [ 99 ]. Genomics can also be leveraged to predict invasion rather than explain it. The resequencing of a global sample of common ragweed ( Ambrosia artemisiifolia L.) elucidated a complex invasion route whereby Europe was invaded by multiple introductions of American ragweed that hybridized in Europe prior to a subsequent introduction to Australia [ 100 , 101 ]. In this context, the use of genomically informed species distribution models helps assess the risk associated with different source populations, which in the case of common ragweed, suggests that a source population from Florida would allow ragweed to invade most of northern Australia [ 102 ]. Globally coordinated research efforts to understand potential distribution models could support the transformation of biosecurity from perspective analysis towards predictive risk assessment.

Herbicide resistance and weed management

Herbicide resistance is among the numerous weedy traits that can evolve in plant populations exposed to agricultural selection pressures. Over-reliance on herbicides to control weeds, along with low diversity and lack of redundancy in weed management strategies, has resulted in globally widespread herbicide resistance [ 103 ]. To date, 272 herbicide-resistant weed species have been reported worldwide, and at least one resistance case exists for 21 of the 31 existing herbicide sites of action [ 104 ]—significantly limiting chemical weed control options available to agriculturalists. This limitation of control options is exacerbated by the recent lack of discovery of herbicides with new sites of action [ 105 ].

Herbicide resistance may result from several different physiological mechanisms. Such mechanisms have been classified into two main groups, target-site resistance (TSR) [ 4 , 106 ] and non-target-site resistance (NTSR) [ 4 , 107 ]. The first group encompasses changes that reduce binding affinity between a herbicide and its target [ 108 ]. These changes may provide resistance to multiple herbicides that have a common biochemical target [ 109 ] and can be effectively managed through mixture and/or rotation of herbicides targeting different sites of action [ 110 ]. The second group (NTSR), includes alterations in herbicide absorption, translocation, sequestration, and/or metabolism that may lead to unpredictable pleotropic cross-resistance profiles where structurally and functionally diverse herbicides are rendered ineffective by one or more genetic variant(s) [ 47 ]. This mechanism of resistance threatens not only the efficacy of existing herbicidal chemistries, but also ones yet to be discovered. While TSR is well understood because of the ease of identification and molecular characterization of target site variants, NTSR mechanisms are significantly more challenging to research because they are often polygenic, and the resistance causing element(s) are not well understood [ 111 ].

Improving the current understanding of metabolic NTSR mechanisms is not an easy task, since genes of diverse biochemical functions are involved, many of which exist as extensive gene families [ 109 , 112 ]. Expression changes of NTSR genes have been implicated in several resistance cases where the protein products of the genes are functionally equivalent across sensitive and resistant plants, but their relative abundance leads to resistance. Thus, regulatory elements of NTSR genes have been scrutinized to understand their role in NTSR mechanisms [ 113 ]. Similarly, epigenetic modifications have been hypothesized to play a role in NTSR, with much remaining to be explored [ 114 , 115 , 116 ]. Untargeted approaches such as genome-wide association, selective sweep scans, linkage mapping, RNA-sequencing, and metabolomic profiling have proven helpful to complement more specific biochemical- and chemo-characterization studies towards the elucidation of NTSR mechanisms as well as their regulation and evolution [ 47 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 ]. Even in cases where resistance has been attributed to TSR, genetic mapping approaches can detect other NTSR loci contributing to resistance (as shown by [ 123 ]) and provide further evidence for the role of TSR mutations across populations. Knowledge of the genetic basis of NTSR will aid the rational design of herbicides by screening new compounds for interaction with newly discovered NTSR proteins during early research phases and by identifying conserved chemical structures that interact with these proteins that should be avoided in small molecule design.

Genomic resources can also be used to predict the protein structure for novel herbicide target site and metabolism genes. This will allow for prediction of efficacy and selectivity for new candidate herbicides in silico to increase herbicide discovery throughput as well as aid in the design and development of next-generation technologies for sustainable weed management. Proteolysis targeting chimeras (PROTACs) have the potential to bind desired targets with great selectivity and degrade proteins by utilizing natural protein ubiquitination and degradation pathways within plants [ 125 ]. Spray-induced gene silencing in weeds using oligonucleotides has potential as a new, innovative, and sustainable method for weed management, but improved methods for design and delivery of oligonucleotides are needed to make this technique a viable management option [ 50 ]. Additionally, success in the field of pharmaceutical drug discovery in the development of molecules modulating protein–protein interactions offers another potential avenue towards the development of herbicides with novel targets [ 126 , 127 ]. High-quality reference genomes allow for the design of new weed management technologies like the ones listed here that are specific to—and effective across—weed species but have a null effect on non-target organisms.

Comparative genomics and genome biology

The genomes of weed species are as diverse as weed species themselves. Weeds are found across highly diverged plant families and often have no phylogenetically close model or crop species relatives for comparison. On all measurable metrics, weed genomes run the gamut. Some have smaller genomes like Cyperus spp. (~ 0.26 Gb) while others are larger, such as Avena fatua (~ 11.1 Gb) (Table  1 ). Some have high heterozygosity in terms of single-nucleotide polymorphisms, such as the Amaranthus spp., while others are primarily self-pollinated and quite homozygous, such as Poa annua [ 128 , 129 ]. Some are diploid such as Conyza canadensis and Echinochloa haploclada while others are polyploid such as C. sumetrensis , E. crus-galli , and E. colona [ 76 ]. The availability of genomic resources in these diverse, unexplored branches of the tree of life allows us to identify consistencies and anomalies in the field of genome biology.

The weed genomes published so far have focused mainly on weeds of agronomic crops, and studies have revolved around their ability to resist key herbicides. For example, genomic resources were vital in the elucidation of herbicide resistance cases involving target site gene copy number variants (CNVs). Gene CNVs of 5-enolpyruvylshikimate-3-phosphate synthase ( EPSPS ) have been found to confer resistance to the herbicide glyphosate in diverse weed species. To date, nine species have independently evolved EPSPS CNVs, and species achieve increased EPSPS copy number via different mechanisms [ 153 ]. For instance, the EPSPS CNV in Bassia scoparia is caused by tandem duplication, which is accredited to transposable element insertions flanking EPSPS and subsequent unequal crossing over events [ 154 , 155 ]. In Eleusine indica , a EPSPS CNV was caused by translocation of the EPSPS locus into the subtelomere followed by telomeric sequence exchange [ 156 ]. One of the most fascinating genome biology discoveries in weed science has been that of extra-chromosomal circular DNAs (eccDNAs) that harbor the EPSPS gene in the weed species Amaranthus palmeri [ 157 , 158 ]. In this case, the eccDNAs autonomously replicate separately from the nuclear genome and do not reintegrate into chromosomes, which has implications for inheritance, fitness, and genome structure [ 159 ]. These discoveries would not have been possible without reference assemblies of weed genomes, next-generation sequencing, and collaboration with experts in plant genomics and bioinformatics.

Another question that is often explored with weedy genomes is the nature and composition of gene families that are associated with NTSR. Gene families under consideration often include cytochrome P450s (CYPs), glutathione- S -transferases (GSTs), ABC transporters, etc. Some questions commonly considered with new weed genomes include how many genes are in each of these gene families, where are they located, and which weed accessions and species have an over-abundance of them that might explain their ability to evolve resistance so rapidly [ 76 , 146 , 160 , 161 ]? Weed genome resources are necessary to answer questions about gene family expansion or contraction during the evolution of weediness, including the role of polyploidy in NTSR gene family expansion as explored by [ 162 ].

Translational research and communication with weed management stakeholders

Whereas genomics of model plants is typically aimed at addressing fundamental questions in plant biology, and genomics of crop species has the obvious goal of crop improvement, goals of genomics of weedy plants also include the development of more effective and sustainable strategies for their management. Weed genomic resources assist with these objectives by providing novel molecular ecological and evolutionary insights from the context of intensive anthropogenic management (which is lacking in model plants), and offer knowledge and resources for trait discovery for crop improvement, especially given that many wild crop relatives are also important agronomic weeds (e.g., [ 163 ]). For instance, crop-wild relatives are valuable for improving crop breeding for marginal environments [ 164 ]. Thus, weed genomics presents unique opportunities and challenges relative to plant genomics more broadly. It should also be noted that although weed science at its core is an applied discipline, it draws broadly from many scientific disciplines such as, plant physiology, chemistry, ecology, and evolutionary biology, to name a few. The successful integration of weed-management strategies, therefore, requires extensive collaboration among individuals collectively possessing the necessary expertise [ 165 ].

With the growing complexity of herbicide resistance management, practitioners are beginning to recognize the importance of understanding resistance mechanisms to inform appropriate management tactics [ 14 ]. Although weed science practitioners do not need to understand the technical details of weed genomics, their appreciation of the power of weed genomics—together with their unique insights from field observations—will yield novel opportunities for applications of weed genomics to weed management. In particular, combining field management history with information on weed resistance mechanisms is expected to provide novel insights into evolutionary trajectories (e.g. [ 6 , 166 ]), which can be utilized for disrupting evolutionary adaptation. It can be difficult to obtain field history information from practitioners, but developing an understanding among them of the importance of such information can be invaluable.

Development of weed genomics resources by the IWGC

Weed genomics is a fast-growing field of research with many recent breakthroughs and many unexplored areas of study. The International Weed Genomics Consortium (IWGC) started in 2021 to address the roadblocks listed above and to promote the study of weedy plants. The IWGC is an open collaboration among academic, government, and industry researchers focused on producing genomic tools for weedy species from around the world. Through this collaboration, our initial aim is to provide chromosome-level reference genome assemblies for at least 50 important weedy species from across the globe that are chosen based on member input, economic impact, and global prevalence (Fig.  1 ). Each genome will include annotation of gene models and repetitive elements and will be freely available through public databases with no intellectual property restrictions. Additionally, future funding of the IWGC will focus on improving gene annotations and supplementing these reference genomes with tools that increase their utility.

The International Weed Genomics Consortium (IWGC) collected input from the weed genomics community to develop plans for weed genome sequencing, annotation, user-friendly genome analysis tools, and community engagement

Reference genomes and data analysis tools

The first objective of the IWGC is to provide high-quality genomic resources for agriculturally important weeds. The IWGC therefore created two main resources for information about, access to, or analysis of weed genomic data (Fig.  1 ). The IWGC website (available at [ 167 ]) communicates the status and results of genome sequencing projects, information on training and funding opportunities, upcoming events, and news in weed genomics. It also contains details of all sequenced species including genome size, ploidy, chromosome number, herbicide resistance status, and reference genome assembly statistics. The IWGC either compiles existing data on genome size, ploidy, and chromosome number, or obtains the data using flow cytometry and cytogenetics (Fig.  1 ; Additional File 2 : Fig S1-S4). Through this website, users can request an account to access our second main resource, an online genome database called WeedPedia (accessible at [ 168 ]), with an account that is created within 3–5 working days of an account request submission. WeedPedia hosts IWGC-generated and other relevant publicly accessible genomic data as well as a suite of bioinformatic tools. Unlike what is available for other fields, weed science did not have a centralized hub for genomics information, data, and analysis prior to the IWGC. Our intention in creating WeedPedia is to encourage collaboration and equity of access to information across the research community. Importantly, all genome assemblies and annotations from the IWGC (Table  1 ), along with the raw data used to produce them, will be made available through NCBI GenBank. Upon completion of a 1-year sponsoring member data confidentiality period for each species (dates listed in Table  1 ), scientific teams within the IWGC produce the first genome-wide investigation to submit for publication including whole genome level analyses on genes, gene families, and repetitive sequences as well as comparative analysis with other species. Genome assemblies and data will be publicly available through NCBI as part of these initial publications for each species.

WeedPedia is a cloud-based omics database management platform built from the software “CropPedia” and licensed from KeyGene (Wageningen, The Netherlands). The interface allows users to access, visualize, and download genome assemblies along with structural and functional annotation. The platform includes a genome browser, comparative map viewer, pangenome tools, RNA-sequencing data visualization tools, genetic mapping and marker analysis tools, and alignment capabilities that allow searches by keyword or sequence. Additionally, genes encoding known target sites of herbicides have been specially annotated, allowing users to quickly identify and compare these genes of interest. The platform is flexible, making it compatible with future integration of other data types such as epigenetic or proteomic information. As an online platform with a graphical user interface, WeedPedia provides user-friendly, intuitive tools that encourage users to integrate genomics into their research while also allowing more advanced users to download genomic data to be used in custom analysis pipelines. We aspire for WeedPedia to mimic the success of other public genomic databases such as NCBI, CoGe, Phytozome, InsectBase, and Mycocosm to name a few. WeedPedia currently hosts reference genomes for 40 species (some of which are currently in their 1-year confidentiality period) with additional genomes in the pipeline to reach a currently planned total of 55 species (Table  1 ). These genomes include both de novo reference genomes generated or in progress by the IWGC (31 species; Table  1 ), and publicly available genome assemblies of 24 weedy or related species that were generated by independent research groups (Table  2 ). As of May 2024, WeedPedia has over 370 registered users from more than 27 countries spread across 6 continents.

The IWGC reference genomes are generated in partnership with the Corteva Agriscience Genome Center of Excellence (Johnston, Iowa) using a combination of single-molecule long-read sequencing, optical genome maps, and chromosome conformation mapping. This strategy has already yielded highly contiguous, phased, chromosome-level assemblies for 26 weed species, with additional assemblies currently in progress (Table  1 ). The IWGC assemblies have been completed as single or haplotype-resolved double-haplotype pseudomolecules in inbreeding and outbreeding species, respectively, with multiple genomes being near gapless. For example, the de novo assemblies of the allohexaploids Conyza sumatrensis and Chenopodium album have all chromosomes captured in single scaffolds and most chromosomes being gapless from telomere to telomere. Complementary full-length isoform (IsoSeq) sequencing of RNA collected from diverse tissue types and developmental stages assists in the development of gene models during annotation.

As with accessibility of data, a core objective of the IWGC is to facilitate open access to sequenced germplasm when possible for featured species. Historically, the weed science community has rarely shared or adopted standard germplasm (e.g., specific weed accessions). The IWGC has selected a specific accession of each species for reference genome assembly (typically susceptible to herbicides). In collaboration with a parallel effort by the Herbicide Resistant Plants committee of the Weed Science Society of America, seeds of the sequenced weed accessions will be deposited in the United States Department of Agriculture Germplasm Resources Information Network [ 186 ] for broad access by the scientific community and their accession numbers will be listed on the IWGC website. In some cases, it is not possible to generate enough seed to deposit into a public repository (e.g., plants that typically reproduce vegetatively, that are self-incompatible, or that produce very few seeds from a single individual). In these cases, the location of collection for sequenced accessions will at least inform the community where the sequenced individual came from and where they may expect to collect individuals with similar genotypes. The IWGC ensures that sequenced accessions are collected and documented to comply with the Nagoya Protocol on access to genetic resources and the fair and equitable sharing of benefits arising from their utilization under the Convention on Biological Diversity and related Access and Benefit Sharing Legislation [ 187 ]. As additional accessions of weed species are sequenced (e.g., pangenomes are obtained), the IWGC will facilitate germplasm sharing protocols to support collaboration. Further, to simplify the investigation of herbicide resistance, the IWGC will link WeedPedia with the International Herbicide-Resistant Weed Database [ 104 ], an already widely known and utilized database for weed scientists.

Training and collaboration in weed genomics

Beyond producing genomic tools and resources, a priority of the IWGC is to enable the utilization of these resources across a wide range of stakeholders. A holistic approach to training is required for weed science generally [ 188 ], and we would argue even more so for weed genomics. To accomplish our training goals, the IWGC is developing and delivering programs aimed at the full range of IWGC stakeholders and covering a breadth of relevant topics. We have taken care to ensure our approaches are diverse as to provide training to researchers with all levels of existing experience and differing reasons for engaging with these tools. Throughout, the focus is on ensuring that our training and outreach result in impacts that benefit a wide range of stakeholders.

Although recently developed tools are incredibly enabling and have great potential to replace antiquated methodology [ 189 ] and to solve pressing weed science problems [ 14 ], specialized computational skills are required to fully explore and unlock meaning from these highly complex datasets. Collaboration with, or training of, computational biologists equipped with these skills and resources developed by the IWGC will enable weed scientists to expand research programs and better understand the genetic underpinnings of weed evolution and herbicide resistance. To fill existing skill gaps, the IWGC is developing summer bootcamps and online modules directed specifically at weed scientists that will provide training on computational skills (Fig.  1 ). Because successful utilization of the IWGC resources requires more than general computational skills, we have created three targeted workshops that teach practical skills related to genomics databases, molecular biology, and population genomics (available at [ 190 ]). The IWGC has also hosted two official conference meetings, one in September of 2021 and one in January of 2023, with more conferences planned. These conferences have included invited speakers to present successful implementations of weed genomics, educational workshops to build computational skills, and networking opportunities for research to connect and collaborate.

Engagement opportunities during undergraduate degrees have been shown to improve academic outcomes [ 191 , 192 ]. As one activity to help achieve this goal, the IWGC has sponsored opportunities for US undergraduates to undertake a 10-week research experience, which includes an introduction to bioinformatics, a plant genomics research project that results in a presentation, and access to career building opportunities in diverse workplace environments. To increase equitable access to conferences and professional communities, we supported early career researchers to attend the first two IWGC conferences in the USA as well as workshops and bootcamps in Europe, South America, and Australia. These hybrid or in-person travel grants are intentionally designed to remove barriers and increase participation of individuals from backgrounds and experiences currently underrepresented within weed/plant science or genomics [ 193 ]. Recipients of these travel awards gave presentations and gained the measurable benefits that come from either virtual or in-person participation in conferences [ 194 ]. Moving forward, weed scientists must amass skills associated with genomic analyses and collaborate with other area experts to fully leverage resources developed by the IWGC.

The tools generated through the IWGC will enable many new research projects with diverse objectives like those listed above. In summary, contiguous genome assemblies and complete annotation information will allow weed scientists to join plant breeders in the use of genetic mapping for many traits including stress tolerance, plant architecture, and herbicide resistance (especially important for cases of NTSR). These assemblies will also allow for investigations of population structure, gene flow, and responses to evolutionary mechanisms like genetic bottlenecking and artificial selection. Understanding gene sequences across diverse weed species will be vital in modeling new herbicide target site proteins and designing novel effective herbicides with minimal off-target effects. The IWGC website will improve accessibility to weed genomics data by providing a single hub for reference genomes as well as phenotypic and genotypic information for accessions shared with the IWGC. Deposition of sequenced germplasm into public repositories will ensure that researchers are able to access and utilize these accessions in their own research to make the field more standardized and equitable. WeedPedia allows users of all backgrounds to quickly access information of interest such as herbicide target site gene sequence or subcellular localization of protein products for different genes. Users can also utilize server-based tools such as BLAST and genome browsing similar to other public genomic databases. Finally, the IWGC is committed to training and connecting weed genomicists through hosting trainings, workshops, and conferences.

Conclusions

Weeds are unique and fascinating plants, having significant impacts on agriculture and ecosystems; and yet, aspects of their biology, ecology, and genetics remain poorly understood. Weeds represent a unique area within plant biology, given their repeated rapid adaptation to sudden and severe shifts in the selective landscape of anthropogenic management practices. The production of a public genomics database with reference genomes and annotations for over 50 weed species represents a substantial step forward towards research goals that improve our understanding of the biology and evolution of weeds. Future work is needed to improve annotations, particularly for complex gene families involved in herbicide detoxification, structural variants, and mobile genetic elements. As reference genome assemblies become available; standard, affordable methods for gathering genotype information will allow for the identification of genetic variants underlying traits of interest. Further, methods for functional validation and hypothesis testing are needed in weeds to validate the effect of genetic variants detected through such experiments, including systems for transformation, gene editing, and transient gene silencing and expression. Future research should focus on utilizing weed genomes to investigate questions about evolutionary biology, ecology, genetics of weedy traits, and weed population dynamics. The IWGC plans to continue the public–private partnership model to host the WeedPedia database over time, integrate new datasets such as genome resequencing and transcriptomes, conduct trainings, and serve as a research coordination network to ensure that advances in weed science from around the world are shared across the research community (Fig.  1 ). Bridging basic plant genomics with translational applications in weeds is needed to deliver on the potential of weed genomics to improve weed management and crop breeding.

Availability of data and materials

All genome assemblies and related sequencing data produced by the IWGC will be available through NCBI as part of publications reporting the first genome-wide analysis for each species.

Gianessi LP, Nathan PR. The value of herbicides in U.S. crop production. Weed Technol. 2007;21(2):559–66.

Article   Google Scholar  

Pimentel D, Lach L, Zuniga R, Morrison D. Environmental and economic costs of nonindigenous species in the United States. Bioscience. 2000;50(1):53–65.

Barrett SH. Crop mimicry in weeds. Econ Bot. 1983;37(3):255–82.

Powles SB, Yu Q. Evolution in action: plants resistant to herbicides. Annu Rev Plant Biol. 2010;61:317–47.

Article   CAS   PubMed   Google Scholar  

Thurber CS, Reagon M, Gross BL, Olsen KM, Jia Y, Caicedo AL. Molecular evolution of shattering loci in U.S. weedy rice. Mol Ecol. 2010;19(16):3271–84.

Article   PubMed   PubMed Central   Google Scholar  

Comont D, Lowe C, Hull R, Crook L, Hicks HL, Onkokesung N, et al. Evolution of generalist resistance to herbicide mixtures reveals a trade-off in resistance management. Nat Commun. 2020;11(1):3086.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Ashworth MB, Walsh MJ, Flower KC, Vila-Aiub MM, Powles SB. Directional selection for flowering time leads to adaptive evolution in Raphanus raphanistrum (wild radish). Evol Appl. 2016;9(4):619–29.

Chan EK, Rowe HC, Kliebenstein DJ. Understanding the evolution of defense metabolites in Arabidopsis thaliana using genome-wide association mapping. Genetics. 2010;185(3):991–1007.

Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM, et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science. 2007;316(5826):889–94.

Harkess A, Zhou J, Xu C, Bowers JE, Van der Hulst R, Ayyampalayam S, et al. The asparagus genome sheds light on the origin and evolution of a young Y chromosome. Nat Commun. 2017;8(1):1279.

Periyannan S, Moore J, Ayliffe M, Bansal U, Wang X, Huang L, et al. The gene Sr33 , an ortholog of barley Mla genes, encodes resistance to wheat stem rust race Ug99. Science. 2013;341(6147):786–8.

Ågren J, Oakley CG, McKay JK, Lovell JT, Schemske DW. Genetic mapping of adaptation reveals fitness tradeoffs in Arabidopsis thaliana . Proc Natl Acad Sci U S A. 2013;110(52):21077–82.

Article   PubMed Central   Google Scholar  

Schartl M, Walter RB, Shen Y, Garcia T, Catchen J, Amores A, et al. The genome of the platyfish, Xiphophorus maculatus , provides insights into evolutionary adaptation and several complex traits. Nat Genet. 2013;45(5):567–72.

Ravet K, Patterson EL, Krähmer H, Hamouzová K, Fan L, Jasieniuk M, et al. The power and potential of genomics in weed biology and management. Pest Manag Sci. 2018;74(10):2216–25.

Hufford MB, Seetharam AS, Woodhouse MR, Chougule KM, Ou S, Liu J, et al. De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes. Science. 2021;373(6555):655–62.

Liao W-W, Asri M, Ebler J, Doerr D, Haukness M, Hickey G, et al. A draft human pangenome reference. Nature. 2023;617(7960):312–24.

Huang Y, Wu D, Huang Z, Li X, Merotto A, Bai L, et al. Weed genomics: yielding insights into the genetics of weedy traits for crop improvement. aBIOTECH. 2023;4:20–30.

Chen K, Yang H, Wu D, Peng Y, Lian L, Bai L, et al. Weed biology and management in the multi-omics era: progress and perspectives. Plant Commun. 2024;5(4):100816.

De Wet JMJ, Harlan JR. Weeds and domesticates: evolution in the man-made habitat. Econ Bot. 1975;29(2):99–108.

Mahaut L, Cheptou PO, Fried G, Munoz F, Storkey J, Vasseur F, et al. Weeds: against the rules? Trends Plant Sci. 2020;25(11):1107–16.

Neve P, Vila-Aiub M, Roux F. Evolutionary-thinking in agricultural weed management. New Phytol. 2009;184(4):783–93.

Article   PubMed   Google Scholar  

Sharma G, Barney JN, Westwood JH, Haak DC. Into the weeds: new insights in plant stress. Trends Plant Sci. 2021;26(10):1050–60.

Vigueira CC, Olsen KM, Caicedo AL. The red queen in the corn: agricultural weeds as models of rapid adaptive evolution. Heredity (Edinb). 2013;110(4):303–11.

Donohue K, Dorn L, Griffith C, Kim E, Aguilera A, Polisetty CR, et al. Niche construction through germination cueing: life-history responses to timing of germination in Arabidopsis thaliana . Evolution. 2005;59(4):771–85.

PubMed   Google Scholar  

Exposito-Alonso M. Seasonal timing adaptation across the geographic range of Arabidopsis thaliana . Proc Natl Acad Sci U S A. 2020;117(18):9665–7.

Fournier-Level A, Korte A, Cooper MD, Nordborg M, Schmitt J, Wilczek AM. A map of local adaptation in Arabidopsis thaliana . Science. 2011;334(6052):86–9.

Hancock AM, Brachi B, Faure N, Horton MW, Jarymowycz LB, Sperone FG, et al. Adaptation to climate across the Arabidopsis thaliana genome. Science. 2011;334(6052):83–6.

Initiative TAG. Analysis of the genome sequence of the flowering plant Arabidopsis thaliana . Nature. 2000;408(6814):796–815.

Alonso-Blanco C, Andrade J, Becker C, Bemm F, Bergelson J, Borgwardt KM, et al. 1,135 genomes reveal the global pattern of polymorphism in Arabidopsis thaliana . Cell. 2016;166(2):481–91.

Durvasula A, Fulgione A, Gutaker RM, Alacakaptan SI, Flood PJ, Neto C, et al. African genomes illuminate the early history and transition to selfing in Arabidopsis thaliana . Proc Natl Acad Sci U S A. 2017;114(20):5213–8.

Frachon L, Mayjonade B, Bartoli C, Hautekèete N-C, Roux F. Adaptation to plant communities across the genome of Arabidopsis thaliana . Mol Biol Evol. 2019;36(7):1442–56.

Fulgione A, Koornneef M, Roux F, Hermisson J, Hancock AM. Madeiran Arabidopsis thaliana reveals ancient long-range colonization and clarifies demography in Eurasia. Mol Biol Evol. 2018;35(3):564–74.

Fulgione A, Neto C, Elfarargi AF, Tergemina E, Ansari S, Göktay M, et al. Parallel reduction in flowering time from de novo mutations enable evolutionary rescue in colonizing lineages. Nat Commun. 2022;13(1):1461.

Kasulin L, Rowan BA, León RJC, Schuenemann VJ, Weigel D, Botto JF. A single haplotype hyposensitive to light and requiring strong vernalization dominates Arabidopsis thaliana populations in Patagonia. Argentina Mol Ecol. 2017;26(13):3389–404.

Picó FX, Méndez-Vigo B, Martínez-Zapater JM, Alonso-Blanco C. Natural genetic variation of Arabidopsis thaliana is geographically structured in the Iberian peninsula. Genetics. 2008;180(2):1009–21.

Atwell S, Huang YS, Vilhjálmsson BJ, Willems G, Horton M, Li Y, et al. Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature. 2010;465(7298):627–31.

Flood PJ, Kruijer W, Schnabel SK, van der Schoor R, Jalink H, Snel JFH, et al. Phenomics for photosynthesis, growth and reflectance in Arabidopsis thaliana reveals circadian and long-term fluctuations in heritability. Plant Methods. 2016;12(1):14.

Marchadier E, Hanemian M, Tisné S, Bach L, Bazakos C, Gilbault E, et al. The complex genetic architecture of shoot growth natural variation in Arabidopsis thaliana . PLoS Genet. 2019;15(4):e1007954.

Tisné S, Serrand Y, Bach L, Gilbault E, Ben Ameur R, Balasse H, et al. Phenoscope: an automated large-scale phenotyping platform offering high spatial homogeneity. Plant J. 2013;74(3):534–44.

Tschiersch H, Junker A, Meyer RC, Altmann T. Establishment of integrated protocols for automated high throughput kinetic chlorophyll fluorescence analyses. Plant Methods. 2017;13:54.

Chen X, MacGregor DR, Stefanato FL, Zhang N, Barros-Galvão T, Penfield S. A VEL3 histone deacetylase complex establishes a maternal epigenetic state controlling progeny seed dormancy. Nat Commun. 2023;14(1):2220.

Choi M, Scholl UI, Ji W, Liu T, Tikhonova IR, Zumbo P, et al. Genetic diagnosis by whole exome capture and massively parallel DNA sequencing. Proc Natl Acad Sci U S A. 2009;106(45):19096–101.

Davey JW, Blaxter ML. RADSeq: next-generation population genetics. Brief Funct Genomics. 2010;9(5–6):416–23.

Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, et al. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE. 2011;6(5):e19379.

MacGregor DR. What makes a weed a weed? How virus-mediated reverse genetics can help to explore the genetics of weediness. Outlooks Pest Manag. 2020;31(5):224–9.

Mellado-Sánchez M, McDiarmid F, Cardoso V, Kanyuka K, MacGregor DR. Virus-mediated transient expression techniques enable gene function studies in blackgrass. Plant Physiol. 2020;183(2):455–9.

Dimaano NG, Yamaguchi T, Fukunishi K, Tominaga T, Iwakami S. Functional characterization of Cytochrome P450 CYP81A subfamily to disclose the pattern of cross-resistance in Echinochloa phyllopogon . Plant Mol Biol. 2020;102(4–5):403–16.

de Figueiredo MRA, Küpper A, Malone JM, Petrovic T, de Figueiredo ABTB, Campagnola G, et al. An in-frame deletion mutation in the degron tail of auxin coreceptor IAA2 confers resistance to the herbicide 2,4-D in Sisymbrium orientale . Proc Natl Acad Sci U S A. 2022;119(9):e2105819119.

Patzoldt WL, Hager AG, McCormick JS, Tranel PJ. A codon deletion confers resistance to herbicides inhibiting protoporphyrinogen oxidase. Proc Natl Acad Sci U S A. 2006;103(33):12329–34.

Zabala-Pardo D, Gaines T, Lamego FP, Avila LA. RNAi as a tool for weed management: challenges and opportunities. Adv Weed Sci. 2022;40(spe1):e020220096.

Fattorini R, Glover BJ. Molecular mechanisms of pollination biology. Annu Rev Plant Biol. 2020;71:487–515.

Rollin O, Benelli G, Benvenuti S, Decourtye A, Wratten SD, Canale A, et al. Weed-insect pollinator networks as bio-indicators of ecological sustainability in agriculture. A review Agron Sustain Dev. 2016;36(1):8.

Irwin RE, Strauss SY. Flower color microevolution in wild radish: evolutionary response to pollinator-mediated selection. Am Nat. 2005;165(2):225–37.

Ma B, Wu J, Shi T-L, Yang Y-Y, Wang W-B, Zheng Y, et al. Lilac ( Syringa oblata ) genome provides insights into its evolution and molecular mechanism of petal color change. Commun Biol. 2022;5(1):686.

Xing A, Wang X, Nazir MF, Zhang X, Wang X, Yang R, et al. Transcriptomic and metabolomic profiling of flavonoid biosynthesis provides novel insights into petals coloration in Asian cotton ( Gossypium arboreum L.). BMC Plant Biol. 2022;22(1):416.

Zheng Y, Chen Y, Liu Z, Wu H, Jiao F, Xin H, et al. Important roles of key genes and transcription factors in flower color differences of Nicotiana alata . Genes (Basel). 2021;12(12):1976.

Krizek BA, Anderson JT. Control of flower size. J Exp Bot. 2013;64(6):1427–37.

Powell AE, Lenhard M. Control of organ size in plants. Curr Biol. 2012;22(9):R360–7.

Spencer V, Kim M. Re"CYC"ling molecular regulators in the evolution and development of flower symmetry. Semin Cell Dev Biol. 2018;79:16–26.

Amrad A, Moser M, Mandel T, de Vries M, Schuurink RC, Freitas L, et al. Gain and loss of floral scent production through changes in structural genes during pollinator-mediated speciation. Curr Biol. 2016;26(24):3303–12.

Delle-Vedove R, Schatz B, Dufay M. Understanding intraspecific variation of floral scent in light of evolutionary ecology. Ann Bot. 2017;120(1):1–20.

Pichersky E, Gershenzon J. The formation and function of plant volatiles: perfumes for pollinator attraction and defense. Curr Opin Plant Biol. 2002;5(3):237–43.

Ballerini ES, Kramer EM, Hodges SA. Comparative transcriptomics of early petal development across four diverse species of Aquilegia reveal few genes consistently associated with nectar spur development. BMC Genom. 2019;20(1):668.

Corbet SA, Willmer PG, Beament JWL, Unwin DM, Prys-Jones OE. Post-secretory determinants of sugar concentration in nectar. Plant Cell Environ. 1979;2(4):293–308.

Galliot C, Hoballah ME, Kuhlemeier C, Stuurman J. Genetics of flower size and nectar volume in Petunia pollination syndromes. Planta. 2006;225(1):203–12.

Vila-Aiub MM, Neve P, Powles SB. Fitness costs associated with evolved herbicide resistance alleles in plants. New Phytol. 2009;184(4):751–67.

Baucom RS. Evolutionary and ecological insights from herbicide-resistant weeds: what have we learned about plant adaptation, and what is left to uncover? New Phytol. 2019;223(1):68–82.

Bajwa AA, Latif S, Borger C, Iqbal N, Asaduzzaman M, Wu H, et al. The remarkable journey of a weed: biology and management of annual ryegrass ( Lolium rigidum ) in conservation cropping systems of Australia. Plants (Basel). 2021;10(8):1505.

Bitarafan Z, Andreasen C. Fecundity allocation in some european weed species competing with crops. Agronomy. 2022;12(5):1196.

Costea M, Weaver SE, Tardif FJ. The biology of Canadian weeds. 130. Amaranthus retroflexus L., A. powellii , A. powellii S. Watson, and A. hybridus L. Can J Plant Sci. 2004;84(2):631–68.

Dixon A, Comont D, Slavov GT, Neve P. Population genomics of selectively neutral genetic structure and herbicide resistance in UK populations of Alopecurus myosuroides . Pest Manag Sci. 2021;77(3):1520–9.

Kersten S, Chang J, Huber CD, Voichek Y, Lanz C, Hagmaier T, et al. Standing genetic variation fuels rapid evolution of herbicide resistance in blackgrass. Proc Natl Acad Sci U S A. 2023;120(16):e2206808120.

Qiu J, Zhou Y, Mao L, Ye C, Wang W, Zhang J, et al. Genomic variation associated with local adaptation of weedy rice during de-domestication. Nat Commun. 2017;8(1):15323.

Kreiner JM, Caballero A, Wright SI, Stinchcombe JR. Selective ancestral sorting and de novo evolution in the agricultural invasion of Amaranthus tuberculatus . Evolution. 2022;76(1):70–85.

Kreiner JM, Latorre SM, Burbano HA, Stinchcombe JR, Otto SP, Weigel D, et al. Rapid weed adaptation and range expansion in response to agriculture over the past two centuries. Science. 2022;378(6624):1079–85.

Wu D, Shen E, Jiang B, Feng Y, Tang W, Lao S, et al. Genomic insights into the evolution of Echinochloa species as weed and orphan crop. Nat Commun. 2022;13(1):689.

Yeaman S, Hodgins KA, Lotterhos KE, Suren H, Nadeau S, Degner JC, et al. Convergent local adaptation to climate in distantly related conifers. Science. 2016;353(6306):1431–3.

Haudry A, Platts AE, Vello E, Hoen DR, Leclercq M, Williamson RJ, et al. An atlas of over 90,000 conserved noncoding sequences provides insight into crucifer regulatory regions. Nat Genet. 2013;45(8):891–8.

Sackton TB, Grayson P, Cloutier A, Hu Z, Liu JS, Wheeler NE, et al. Convergent regulatory evolution and loss of flight in paleognathous birds. Science. 2019;364(6435):74–8.

Ye CY, Fan L. Orphan crops and their wild relatives in the genomic era. Mol Plant. 2021;14(1):27–39.

Clements DR, Jones VL. Ten ways that weed evolution defies human management efforts amidst a changing climate. Agronomy. 2021;11(2):284.

Article   CAS   Google Scholar  

Weinig C. Rapid evolutionary responses to selection in heterogeneous environments among agricultural and nonagricultural weeds. Int J Plant Sci. 2005;166(4):641–7.

Cousens RD, Fournier-Level A. Herbicide resistance costs: what are we actually measuring and why? Pest Manag Sci. 2018;74(7):1539–46.

Lasky JR, Josephs EB, Morris GP. Genotype–environment associations to reveal the molecular basis of environmental adaptation. Plant Cell. 2023;35(1):125–38.

Lotterhos KE. The effect of neutral recombination variation on genome scans for selection. G3-Genes Genom Genet. 2019;9(6):1851–67.

Lovell JT, MacQueen AH, Mamidi S, Bonnette J, Jenkins J, Napier JD, et al. Genomic mechanisms of climate adaptation in polyploid bioenergy switchgrass. Nature. 2021;590(7846):438–44.

Todesco M, Owens GL, Bercovich N, Légaré J-S, Soudi S, Burge DO, et al. Massive haplotypes underlie ecotypic differentiation in sunflowers. Nature. 2020;584(7822):602–7.

Revolinski SR, Maughan PJ, Coleman CE, Burke IC. Preadapted to adapt: Underpinnings of adaptive plasticity revealed by the downy brome genome. Commun Biol. 2023;6(1):326.

Kuester A, Conner JK, Culley T, Baucom RS. How weeds emerge: a taxonomic and trait-based examination using United States data. New Phytol. 2014;202(3):1055–68.

Arnaud JF, Fénart S, Cordellier M, Cuguen J. Populations of weedy crop-wild hybrid beets show contrasting variation in mating system and population genetic structure. Evol Appl. 2010;3(3):305–18.

Ellstrand NC, Schierenbeck KA. Hybridization as a stimulus for the evolution of invasiveness in plants? Proc Natl Acad Sci U S A. 2000;97(13):7043–50.

Nakabayashi K, Leubner-Metzger G. Seed dormancy and weed emergence: from simulating environmental change to understanding trait plasticity, adaptive evolution, and population fitness. J Exp Bot. 2021;72(12):4181–5.

Busi R, Yu Q, Barrett-Lennard R, Powles S. Long distance pollen-mediated flow of herbicide resistance genes in Lolium rigidum . Theor Appl Genet. 2008;117(8):1281–90.

Délye C, Clément JAJ, Pernin F, Chauvel B, Le Corre V. High gene flow promotes the genetic homogeneity of arable weed populations at the landscape level. Basic Appl Ecol. 2010;11(6):504–12.

Roumet M, Noilhan C, Latreille M, David J, Muller MH. How to escape from crop-to-weed gene flow: phenological variation and isolation-by-time within weedy sunflower populations. New Phytol. 2013;197(2):642–54.

Moghadam SH, Alebrahim MT, Mohebodini M, MacGregor DR. Genetic variation of Amaranthus retroflexus L. and Chenopodium album L. (Amaranthaceae) suggests multiple independent introductions into Iran. Front Plant Sci. 2023;13:1024555.

Muller M-H, Latreille M, Tollon C. The origin and evolution of a recent agricultural weed: population genetic diversity of weedy populations of sunflower ( Helianthus annuus L.) in Spain and France. Evol Appl. 2011;4(3):499–514.

Wesse C, Welk E, Hurka H, Neuffer B. Geographical pattern of genetic diversity in Capsella bursa-pastoris (Brassicaceae) -A global perspective. Ecol Evol. 2021;11(1):199–213.

Fraimout A, Debat V, Fellous S, Hufbauer RA, Foucaud J, Pudlo P, et al. Deciphering the routes of invasion of Drosophila suzukii by means of ABC random forest. Mol Biol Evol. 2017;34(4):980–96.

CAS   PubMed   PubMed Central   Google Scholar  

Battlay P, Wilson J, Bieker VC, Lee C, Prapas D, Petersen B, et al. Large haploblocks underlie rapid adaptation in the invasive weed Ambrosia artemisiifolia . Nat Commun. 2023;14(1):1717.

van Boheemen LA, Hodgins KA. Rapid repeatable phenotypic and genomic adaptation following multiple introductions. Mol Ecol. 2020;29(21):4102–17.

Putra A, Hodgins K, Fournier-Level A. Assessing the invasive potential of different source populations of ragweed ( Ambrosia artemisiifolia L.) through genomically-informed species distribution modelling. Authorea. 2023;17(1):e13632.

Google Scholar  

Bourguet D, Delmotte F, Franck P, Guillemaud T, Reboud X, Vacher C, et al. Heterogeneity of selection and the evolution of resistance. Trends Ecol Evol. 2013;28(2):110–8.

The International Herbicide-Resistant Weed Database. www.weedscience.org . Accessed 20 June 2023.

Powles S. Herbicide discovery through innovation and diversity. Adv Weed Sci. 2022;40(spe1):e020220074.

Murphy BP, Tranel PJ. Target-site mutations conferring herbicide resistance. Plants (Basel). 2019;8(10):382.

Gaines TA, Duke SO, Morran S, Rigon CAG, Tranel PJ, Küpper A, et al. Mechanisms of evolved herbicide resistance. J Biol Chem. 2020;295(30):10307–30.

Lonhienne T, Cheng Y, Garcia MD, Hu SH, Low YS, Schenk G, et al. Structural basis of resistance to herbicides that target acetohydroxyacid synthase. Nat Commun. 2022;13(1):3368.

Comont D, MacGregor DR, Crook L, Hull R, Nguyen L, Freckleton RP, et al. Dissecting weed adaptation: fitness and trait correlations in herbicide-resistant Alopecurus myosuroides . Pest Manag Sci. 2022;78(7):3039–50.

Neve P. Simulation modelling to understand the evolution and management of glyphosate resistance in weeds. Pest Manag Sci. 2008;64(4):392–401.

Torra J, Alcántara-de la Cruz R. Molecular mechanisms of herbicide resistance in weeds. Genes (Basel). 2022;13(11):2025.

Délye C, Gardin JAC, Boucansaud K, Chauvel B, Petit C. Non-target-site-based resistance should be the centre of attention for herbicide resistance research: Alopecurus myosuroides as an illustration. Weed Res. 2011;51(5):433–7.

Chandra S, Leon RG. Genome-wide evolutionary analysis of putative non-specific herbicide resistance genes and compilation of core promoters between monocots and dicots. Genes (Basel). 2022;13(7):1171.

Margaritopoulou T, Tani E, Chachalis D, Travlos I. Involvement of epigenetic mechanisms in herbicide resistance: the case of Conyza canadensis . Agriculture. 2018;8(1):17.

Pan L, Guo Q, Wang J, Shi L, Yang X, Zhou Y, et al. CYP81A68 confers metabolic resistance to ALS and ACCase-inhibiting herbicides and its epigenetic regulation in Echinochloa crus-galli . J Hazard Mater. 2022;428:128225.

Sen MK, Hamouzová K, Košnarová P, Roy A, Soukup J. Herbicide resistance in grass weeds: Epigenetic regulation matters too. Front Plant Sci. 2022;13:1040958.

Han H, Yu Q, Beffa R, González S, Maiwald F, Wang J, et al. Cytochrome P450 CYP81A10v7 in Lolium rigidum confers metabolic resistance to herbicides across at least five modes of action. Plant J. 2021;105(1):79–92.

Kubis GC, Marques RZ, Kitamura RS, Barroso AA, Juneau P, Gomes MP. Antioxidant enzyme and Cytochrome P450 activities are involved in horseweed ( Conyza sumatrensis ) resistance to glyphosate. Stress. 2023;3(1):47–57.

Qiao Y, Zhang N, Liu J, Yang H. Interpretation of ametryn biodegradation in rice based on joint analyses of transcriptome, metabolome and chemo-characterization. J Hazard Mater. 2023;445:130526.

Rouse CE, Roma-Burgos N, Barbosa Martins BA. Physiological assessment of non–target site restistance in multiple-resistant junglerice ( Echinochloa colona ). Weed Sci. 2019;67(6):622–32.

Abou-Khater L, Maalouf F, Jighly A, Alsamman AM, Rubiales D, Rispail N, et al. Genomic regions associated with herbicide tolerance in a worldwide faba bean ( Vicia faba L.) collection. Sci Rep. 2022;12(1):158.

Gupta S, Harkess A, Soble A, Van Etten M, Leebens-Mack J, Baucom RS. Interchromosomal linkage disequilibrium and linked fitness cost loci associated with selection for herbicide resistance. New Phytol. 2023;238(3):1263–77.

Kreiner JM, Tranel PJ, Weigel D, Stinchcombe JR, Wright SI. The genetic architecture and population genomic signatures of glyphosate resistance in Amaranthus tuberculatus . Mol Ecol. 2021;30(21):5373–89.

Parcharidou E, Dücker R, Zöllner P, Ries S, Orru R, Beffa R. Recombinant glutathione transferases from flufenacet-resistant black-grass ( Alopecurus myosuroides Huds.) form different flufenacet metabolites and differ in their interaction with pre- and post-emergence herbicides. Pest Manag Sci. 2023;79(9):3376–86.

Békés M, Langley DR, Crews CM. PROTAC targeted protein degraders: the past is prologue. Nat Rev Drug Discov. 2022;21(3):181–200.

Acuner Ozbabacan SE, Engin HB, Gursoy A, Keskin O. Transient protein-protein interactions. Protein Eng Des Sel. 2011;24(9):635–48.

Lu H, Zhou Q, He J, Jiang Z, Peng C, Tong R, et al. Recent advances in the development of protein–protein interactions modulators: mechanisms and clinical trials. Signal Transduct Target Ther. 2020;5(1):213.

Benson CW, Sheltra MR, Maughan PJ, Jellen EN, Robbins MD, Bushman BS, et al. Homoeologous evolution of the allotetraploid genome of Poa annua L. BMC Genom. 2023;24(1):350.

Robbins MD, Bushman BS, Huff DR, Benson CW, Warnke SE, Maughan CA, et al. Chromosome-scale genome assembly and annotation of allotetraploid annual bluegrass ( Poa annua L.). Genome Biol Evol. 2022;15(1):evac180.

Montgomery JS, Giacomini D, Waithaka B, Lanz C, Murphy BP, Campe R, et al. Draft genomes of Amaranthus tuberculatus , Amaranthus hybridus and Amaranthus palmeri . Genome Biol Evol. 2020;12(11):1988–93.

Jeschke MR, Tranel PJ, Rayburn AL. DNA content analysis of smooth pigweed ( Amaranthus hybridus ) and tall waterhemp ( A. tuberculatus ): implications for hybrid detection. Weed Sci. 2003;51(1):1–3.

Rayburn AL, McCloskey R, Tatum TC, Bollero GA, Jeschke MR, Tranel PJ. Genome size analysis of weedy Amaranthus species. Crop Sci. 2005;45(6):2557–62.

Laforest M, Martin SL, Bisaillon K, Soufiane B, Meloche S, Tardif FJ, et al. The ancestral karyotype of the Heliantheae Alliance, herbicide resistance, and human allergens: Insights from the genomes of common and giant ragweed. Plant Genome . 2024;e20442. https://doi.org/10.1002/tpg2.20442 .

Mulligan GA. Chromosome numbers of Canadian weeds. I Canad J Bot. 1957;35(5):779–89.

Meyer L, Causse R, Pernin F, Scalone R, Bailly G, Chauvel B, et al. New gSSR and EST-SSR markers reveal high genetic diversity in the invasive plant Ambrosia artemisiifolia L. and can be transferred to other invasive Ambrosia species. PLoS One. 2017;12(5):e0176197.

Pustahija F, Brown SC, Bogunić F, Bašić N, Muratović E, Ollier S, et al. Small genomes dominate in plants growing on serpentine soils in West Balkans, an exhaustive study of 8 habitats covering 308 taxa. Plant Soil. 2013;373(1):427–53.

Kubešová M, Moravcova L, Suda J, Jarošík V, Pyšek P. Naturalized plants have smaller genomes than their non-invading relatives: a flow cytometric analysis of the Czech alien flora. Preslia. 2010;82(1):81–96.

Thébaud C, Abbott RJ. Characterization of invasive Conyza species (Asteraceae) in Europe: quantitative trait and isozyme analysis. Am J Bot. 1995;82(3):360–8.

Garcia S, Hidalgo O, Jakovljević I, Siljak-Yakovlev S, Vigo J, Garnatje T, et al. New data on genome size in 128 Asteraceae species and subspecies, with first assessments for 40 genera, 3 tribes and 2 subfamilies. Plant Biosyst. 2013;147(4):1219–27.

Zhao X, Yi L, Ren Y, Li J, Ren W, Hou Z, et al. Chromosome-scale genome assembly of the yellow nutsedge ( Cyperus esculentus ). Genome Biol Evol. 2023;15(3):evad027.

Bennett MD, Leitch IJ, Hanson L. DNA amounts in two samples of angiosperm weeds. Ann Bot. 1998;82:121–34.

Schulz-Schaeffer J, Gerhardt S. Cytotaxonomic analysis of the Euphorbia spp. (leafy spurge) complex. II: Comparative study of the chromosome morphology. Biol Zentralbl. 1989;108(1):69–76.

Schaeffer JR, Gerhardt S. The impact of introgressive hybridization on the weediness of leafy spurge. Leafy Spurge Symposium. 1989;1989:97–105.

Bai C, Alverson WS, Follansbee A, Waller DM. New reports of nuclear DNA content for 407 vascular plant taxa from the United States. Ann Bot. 2012;110(8):1623–9.

Aarestrup JR, Karam D, Fernandes GW. Chromosome number and cytogenetics of Euphorbia heterophylla L. Genet Mol Res. 2008;7(1):217–22.

Wang L, Sun X, Peng Y, Chen K, Wu S, Guo Y, et al. Genomic insights into the origin, adaptive evolution, and herbicide resistance of Leptochloa chinensis , a devastating tetraploid weedy grass in rice fields. Mol Plant. 2022;15(6):1045–58.

Paril J, Pandey G, Barnett EM, Rane RV, Court L, Walsh T, et al. Rounding up the annual ryegrass genome: high-quality reference genome of Lolium rigidum . Front Genet. 2022;13:1012694.

Weiss-Schneeweiss H, Greilhuber J, Schneeweiss GM. Genome size evolution in holoparasitic Orobanche (Orobanchaceae) and related genera. Am J Bot. 2006;93(1):148–56.

Towers G, Mitchell J, Rodriguez E, Bennett F, Subba Rao P. Biology & chemistry of Parthenium hysterophorus L., a problem weed in India. Biol Rev. 1977;48:65–74.

CAS   Google Scholar  

Moghe GD, Hufnagel DE, Tang H, Xiao Y, Dworkin I, Town CD, et al. Consequences of whole-genome triplication as revealed by comparative genomic analyses of the wild radish ( Raphanus raphanistrum ) and three other Brassicaceae species. Plant Cell. 2014;26(5):1925–37.

Zhang X, Liu T, Wang J, Wang P, Qiu Y, Zhao W, et al. Pan-genome of Raphanus highlights genetic variation and introgression among domesticated, wild, and weedy radishes. Mol Plant. 2021;14(12):2032–55.

Chytrý M, Danihelka J, Kaplan Z, Wild J, Holubová D, Novotný P, et al. Pladias database of the Czech flora and vegetation. Preslia. 2021;93(1):1–87.

Patterson EL, Pettinga DJ, Ravet K, Neve P, Gaines TA. Glyphosate resistance and EPSPS gene duplication: Convergent evolution in multiple plant species. J Hered. 2018;109(2):117–25.

Jugulam M, Niehues K, Godar AS, Koo DH, Danilova T, Friebe B, et al. Tandem amplification of a chromosomal segment harboring 5-enolpyruvylshikimate-3-phosphate synthase locus confers glyphosate resistance in Kochia scoparia . Plant Physiol. 2014;166(3):1200–7.

Patterson EL, Saski CA, Sloan DB, Tranel PJ, Westra P, Gaines TA. The draft genome of Kochia scoparia and the mechanism of glyphosate resistance via transposon-mediated EPSPS tandem gene duplication. Genome Biol Evol. 2019;11(10):2927–40.

Zhang C, Johnson N, Hall N, Tian X, Yu Q, Patterson E. Subtelomeric 5-enolpyruvylshikimate-3-phosphate synthase ( EPSPS ) copy number variation confers glyphosate resistance in Eleusine indica . Nat Commun. 2023;14:4865.

Koo D-H, Molin WT, Saski CA, Jiang J, Putta K, Jugulam M, et al. Extrachromosomal circular DNA-based amplification and transmission of herbicide resistance in crop weed Amaranthus palmeri . Proc Natl Acad Sci U S A. 2018;115(13):3332–7.

Molin WT, Yaguchi A, Blenner M, Saski CA. The eccDNA Replicon: A heritable, extranuclear vehicle that enables gene amplification and glyphosate resistance in Amaranthus palmeri . Plant Cell. 2020;32(7):2132–40.

Jugulam M. Can non-Mendelian inheritance of extrachromosomal circular DNA-mediated EPSPS gene amplification provide an opportunity to reverse resistance to glyphosate? Weed Res. 2021;61(2):100–5.

Kreiner JM, Giacomini DA, Bemm F, Waithaka B, Regalado J, Lanz C, et al. Multiple modes of convergent adaptation in the spread of glyphosate-resistant Amaranthus tuberculatus . Proc Natl Acad Sci U S A. 2019;116(42):21076–84.

Cai L, Comont D, MacGregor D, Lowe C, Beffa R, Neve P, et al. The blackgrass genome reveals patterns of non-parallel evolution of polygenic herbicide resistance. New Phytol. 2023;237(5):1891–907.

Chen K, Yang H, Peng Y, Liu D, Zhang J, Zhao Z, et al. Genomic analyses provide insights into the polyploidization-driven herbicide adaptation in Leptochloa weeds. Plant Biotechnol J. 2023;21(8):1642–58.

Ohadi S, Hodnett G, Rooney W, Bagavathiannan M. Gene flow and its consequences in Sorghum spp. Crit Rev Plant Sci. 2017;36(5–6):367–85.

Renzi JP, Coyne CJ, Berger J, von Wettberg E, Nelson M, Ureta S, et al. How could the use of crop wild relatives in breeding increase the adaptation of crops to marginal environments? Front Plant Sci. 2022;13:886162.

Ward SM, Cousens RD, Bagavathiannan MV, Barney JN, Beckie HJ, Busi R, et al. Agricultural weed research: a critique and two proposals. Weed Sci. 2014;62(4):672–8.

Evans JA, Tranel PJ, Hager AG, Schutte B, Wu C, Chatham LA, et al. Managing the evolution of herbicide resistance. Pest Manag Sci. 2016;72(1):74–80.

International Weed Genomics Consortium Website. https://www.weedgenomics.org . Accessed 20 June 2023.

WeedPedia Database. https://weedpedia.weedgenomics.org/ . Accessed 20 June 2023.

Hall N, Chen J, Matzrafi M, Saski CA, Westra P, Gaines TA, et al. FHY3/FAR1 transposable elements generate adaptive genetic variation in the Bassia scoparia genome. bioRxiv . 2023; DOI: https://doi.org/10.1101/2023.05.26.542497 .

Jarvis DE, Sproul JS, Navarro-Domínguez B, Krak K, Jaggi K, Huang Y-F, et al. Chromosome-scale genome assembly of the hexaploid Taiwanese goosefoot “Djulis” ( Chenopodium formosanum ). Genome Biol Evol. 2022;14(8):evac120.

Ferreira LAI, de Oliveira RS, Jr., Constantin J, Brunharo C. Evolution of ACCase-inhibitor resistance in Chloris virgata is conferred by a Trp2027Cys mutation in the herbicide target site. Pest Manag Sci. 2023;79(12):5220–9.

Laforest M, Martin SL, Bisaillon K, Soufiane B, Meloche S, Page E. A chromosome-scale draft sequence of the Canada fleabane genome. Pest Manag Sci. 2020;76(6):2158–69.

Guo L, Qiu J, Ye C, Jin G, Mao L, Zhang H, et al. Echinochloa crus-galli genome analysis provides insight into its adaptation and invasiveness as a weed. Nat Commun. 2017;8(1):1031.

Sato MP, Iwakami S, Fukunishi K, Sugiura K, Yasuda K, Isobe S, et al. Telomere-to-telomere genome assembly of an allotetraploid pernicious weed, Echinochloa phyllopogon . DNA Res. 2023;30(5):dsad023.

Stein JC, Yu Y, Copetti D, Zwickl DJ, Zhang L, Zhang C, et al. Genomes of 13 domesticated and wild rice relatives highlight genetic conservation, turnover and innovation across the genus Oryza . Nat Genet. 2018;50(2):285–96.

Wu D, Xie L, Sun Y, Huang Y, Jia L, Dong C, et al. A syntelog-based pan-genome provides insights into rice domestication and de-domestication. Genome Biol. 2023;24(1):179.

Wang Z, Huang S, Yang Z, Lai J, Gao X, Shi J. A high-quality, phased genome assembly of broomcorn millet reveals the features of its subgenome evolution and 3D chromatin organization. Plant Commun. 2023;4(3):100557.

Mao Q, Huff DR. The evolutionary origin of Poa annua L. Crop Sci. 2012;52(4):1910–22.

Benson CW, Sheltra MR, Maughan JP, Jellen EN, Robbins MD, Bushman BS, et al. Homoeologous evolution of the allotetraploid genome of Poa annua L. Res Sq. 2023. https://doi.org/10.21203/rs.3.rs-2729084/v1 .

Brunharo C, Benson CW, Huff DR, Lasky JR. Chromosome-scale genome assembly of Poa trivialis and population genomics reveal widespread gene flow in a cool-season grass seed production system. Plant Direct. 2024;8(3):e575.

Mo C, Wu Z, Shang X, Shi P, Wei M, Wang H, et al. Chromosome-level and graphic genomes provide insights into metabolism of bioactive metabolites and cold-adaption of Pueraria lobata var. montana . DNA Research. 2022;29(5):dsac030.

Thielen PM, Pendleton AL, Player RA, Bowden KV, Lawton TJ, Wisecaver JH. Reference genome for the highly transformable Setaria viridis ME034V. G3 (Bethesda, Md). 2020;10(10):3467–78.

Yoshida S, Kim S, Wafula EK, Tanskanen J, Kim Y-M, Honaas L, et al. Genome sequence of Striga asiatica provides insight into the evolution of plant parasitism. Curr Biol. 2019;29(18):3041–52.

Qiu S, Bradley JM, Zhang P, Chaudhuri R, Blaxter M, Butlin RK, et al. Genome-enabled discovery of candidate virulence loci in Striga hermonthica , a devastating parasite of African cereal crops. New Phytol. 2022;236(2):622–38.

Nunn A, Rodríguez-Arévalo I, Tandukar Z, Frels K, Contreras-Garrido A, Carbonell-Bejerano P, et al. Chromosome-level Thlaspi arvense genome provides new tools for translational research and for a newly domesticated cash cover crop of the cooler climates. Plant Biotechnol J. 2022;20(5):944–63.

USDA-ARS Germplasm Resources Information Network (GRIN). https://www.ars-grin.gov/ . Accessed 20 June 2023.

Buck M, Hamilton C. The Nagoya Protocol on access to genetic resources and the fair and equitable sharing of benefits arising from their utilization to the convention on biological diversity. RECIEL. 2011;20(1):47–61.

Chauhan BS, Matloob A, Mahajan G, Aslam F, Florentine SK, Jha P. Emerging challenges and opportunities for education and research in weed science. Front Plant Sci. 2017;8:1537.

Shah S, Lonhienne T, Murray CE, Chen Y, Dougan KE, Low YS, et al. Genome-guided analysis of seven weed species reveals conserved sequence and structural features of key gene targets for herbicide development. Front Plant Sci. 2022;13:909073.

International Weed Genomics Consortium Training Resources. https://www.weedgenomics.org/training-resources/ . Accessed 20 June 2023.

Blackford S. Harnessing the power of communities: career networking strategies for bioscience PhD students and postdoctoral researchers. FEMS Microbiol Lett. 2018;365(8):fny033.

Pender M, Marcotte DE, Sto Domingo MR, Maton KI. The STEM pipeline: The role of summer research experience in minority students’ Ph.D. aspirations. Educ Policy Anal Arch. 2010;18(30):1–36.

PubMed   PubMed Central   Google Scholar  

Burke A, Okrent A, Hale K. The state of U.S. science and engineering 2022. Foundation NS. https://ncses.nsf.gov/pubs/nsb20221 . 2022.

Wu J-Y, Liao C-H, Cheng T, Nian M-W. Using data analytics to investigate attendees’ behaviors and psychological states in a virtual academic conference. Educ Technol Soc. 2021;24(1):75–91.

Download references

Peer review information

Wenjing She was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

The International Weed Genomics Consortium is supported by BASF SE, Bayer AG, Syngenta Ltd, Corteva Agriscience, CropLife International (Global Herbicide Resistance Action Committee), the Foundation for Food and Agriculture Research (Award DSnew-0000000024), and two conference grants from USDA-NIFA (Award numbers 2021–67013-33570 and 2023-67013-38785).

Author information

Authors and affiliations.

Department of Agricultural Biology, Colorado State University, 1177 Campus Delivery, Fort Collins, CO, 80523, USA

Jacob Montgomery, Sarah Morran & Todd A. Gaines

Protecting Crops and the Environment, Rothamsted Research, Harpenden, Hertfordshire, UK

Dana R. MacGregor

Department of Crop, Soil, and Environmental Sciences, Auburn University, Auburn, AL, USA

J. Scott McElroy

Department of Plant and Environmental Sciences, University of Copenhagen, Taastrup, Denmark

Paul Neve & Célia Neto

IFEVA-Conicet-Department of Ecology, University of Buenos Aires, Buenos Aires, Argentina

Martin M. Vila-Aiub & Maria Victoria Sandoval

Department of Ecology, Faculty of Agronomy, University of Buenos Aires, Buenos Aires, Argentina

Analia I. Menéndez

Department of Botany, The University of British Columbia, Vancouver, BC, Canada

Julia M. Kreiner

Institute of Crop Sciences, Zhejiang University, Hangzhou, China

Longjiang Fan

Department of Biology, University of Massachusetts Amherst, Amherst, MA, USA

Ana L. Caicedo

Department of Plant and Wildlife Sciences, Brigham Young University, Provo, UT, USA

Peter J. Maughan

Bayer AG, Weed Control Research, Frankfurt, Germany

Bianca Assis Barbosa Martins, Jagoda Mika, Alberto Collavo & Bodo Peters

Department of Crop Sciences, Federal University of Rio Grande Do Sul, Porto Alegre, Rio Grande Do Sul, Brazil

Aldo Merotto Jr.

Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, USA

Nithya K. Subramanian & Muthukumar V. Bagavathiannan

Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, USA

Luan Cutti & Eric L. Patterson

Department of Agronomy, Kansas State University, Manhattan, KS, USA

Md. Mazharul Islam & Mithila Jugulam

Department of Plant Pathology, Kansas State University, Manhattan, KS, USA

Bikram S. Gill

Crop Protection Discovery and Development, Corteva Agriscience, Indianapolis, IN, USA

Robert Cicchillo, Roger Gast & Neeta Soni

Genome Center of Excellence, Corteva Agriscience, Johnston, IA, USA

Terry R. Wright, Gina Zastrow-Hayes, Gregory May, Kevin Fengler & Victor Llaca

School of Agriculture, Food and Wine, University of Adelaide, Glen Osmond, South Australia, Australia

Jenna M. Malone

Jealott’s Hill International Research Centre, Syngenta Ltd, Bracknell, Berkshire, UK

Deepmala Sehgal, Shiv Shankhar Kaundun & Richard P. Dale

Department of Plant and Soil Sciences, University of Pretoria, Pretoria, South Africa

Barend Juan Vorster

BASF SE, Ludwigshafen Am Rhein, Germany

Jens Lerchl

Department of Crop Sciences, University of Illinois, Urbana, IL, USA

Patrick J. Tranel

Senior Scientist Consultant, Herbicide Resistance Action Committee / CropLife International, Liederbach, Germany

Roland Beffa

School of BioSciences, University of Melbourne, Parkville, VIC, Australia

Alexandre Fournier-Level

You can also search for this author in PubMed   Google Scholar

Contributions

JMo and TG conceived and outlined the article. TG, DM, EP, RB, JSM, PJT, MJ wrote grants to obtain funding. MMI, BSG, and MJ performed mitotic chromosome visualization. VL performed sequencing. VL and KF assembled the genomes. LC and ELP annotated the genomes. JMo, SM, DRM, JSM, PN, CN, MV, MVS, AIM, JMK, LF, ALC, PJM, BABM, JMi, AC, MVB, LC, AFL, and ELP wrote the first draft of the article. All authors edited the article and improved the final version.

Corresponding author

Correspondence to Todd A. Gaines .

Ethics declarations

Ethics approval and consent to participate.

Ethical approval is not applicable for this article.

Competing interests

Some authors work for commercial agricultural companies (BASF, Bayer, Corteva Agriscience, or Syngenta) that develop and sell weed control products.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

13059_2024_3274_moesm1_esm.docx.

Additional file 1. List of completed and in-progress genome assemblies of weed species pollinated by insects (Table S1).

13059_2024_3274_MOESM2_ESM.docx

Additional file 2. Methods and results for visualizing and counting the metaphase chromosomes of hexaploid Avena fatua (Fig S1); diploid Lolium rigidum  (Fig S2); tetraploid Phalaris minor (Fig S3); and tetraploid Salsola tragus (Fig S4).

Additional file 3. Review history.

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Montgomery, J., Morran, S., MacGregor, D.R. et al. Current status of community resources and priorities for weed genomics research. Genome Biol 25 , 139 (2024). https://doi.org/10.1186/s13059-024-03274-y

Download citation

Received : 11 July 2023

Accepted : 13 May 2024

Published : 27 May 2024

DOI : https://doi.org/10.1186/s13059-024-03274-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Weed science
• Reference genomes
• Rapid adaptation
• Herbicide resistance
• Public resources

Genome Biology

ISSN: 1474-760X

• Submission enquiries: [email protected]
• General enquiries: [email protected]