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Capstone Components

12 Research Design

The story continues….

“So, how do we go about answering our research questions?” asked Harry.

Physicus explained that they will have to analyze their questions to see what types of answers are required. Knowing this will guide their decisions about how to design the needs assessment to answer their questions.

“There are two basic types of answers to research questions, quantitative and qualitative. The types of answers the questions require tell us what type of research design we need,” said Physicus.

“I guess if I ask how we decide which type of research design we should choose, you will say, ‘It depends?'” uttered Harry.

Physicus’ face brightened as he blurted out, “Absolutely not! Negative!” Physicus continued, “If the research questions are stated well, there will only be two ways in which they can be answered. The research questions are king; they make all the decisions.”

“How come?” Harry appeared confused.

“Well, let us see. Think about our first question. How many mice will Pickles attack at one time? What type of answer does this question require? It requires a numeric answer, correct?” Physicus asked.

“Yes, that is correct,” Harry said.

Physicus continued, “Good. So, does our second question also require a numeric answer?”

“The second question is also answered with a number,” replied Harry

Physicus blurted, “Correct! This means we need to use a quantitative research design!”

Physicus continued, “Now if we had research questions that could not be answered with numbers, we would need to use a qualitative research design to answer our questions with words or phrases instead.”

Harry now appeared relieved, “I get it. So in designing a research project, we simply look for a way to answer the research questions. That’s easy!”

“Well, it depends,” answered Physicus smiling.

Interpreting the Story

There are qualitative, quantitative, mixed methods, and applied research designs. Based on the research questions, the research design will be obvious. Physicus led Harry in determining their investigation would need a quantitative design, because they only needed numerical data to answer their research questions. If Harry’s questions could only be answered with words or phrases, then a qualitative design would be needed. If the friends had questions needing to be answered with numbers and phrases, then either a mixed methods or an applied research design would have been the choice.

Research Design

The Research Design explains what type of research is being conducted in the needs assessment. The writing in this heading also explains why this type of research is needed to obtain the answers to the research or guiding questions for the project. The design provides a blueprint for the methodology. Articulating the nature of the research design is critical for explaining the Methodology (see the next chapter).

There are four categories of research designs used in educational research and a variety of specific research designs in each category. The first step in determining which category to use is to identify what type of data will answer the research questions. As in our story, Harry and Physicus had research questions that required quantitative answers, so the category of their research design is quantitative.

The next step in finding the specific research design is to consider the purpose (goal) of the research project. The research design must support the purpose. In our story, Harry and Physicus need a quantitative research design that supports their goal of determining the effect of the number of mice Pickles encounters at one time on his behavior.  A causal-comparative or quasi-experimental research design is the best choice for the friends because these are specific quantitative designs used to find a cause-and-effect relationship.

Quantitative Research Designs

Quantitative research designs seek results based on statistical analyses of the collected numerical data. The primary quantitative designs used in educational research include descriptive, correlational, causal-comparative, and quasi-experimental designs. Numerical data are collected and analyzed using statistical calculations appropriate for the design. For example, analyses like mean, median, mode, range, etc. are used to describe or explain a phenomenon observed in a descriptive research design. A correlational research design uses statistics, such as correlation coefficient or regression analyses to explain how two phenomena are related. Causal-comparative and quasi-experimental designs use analyses needed to establish causal relationships, such as pre-post testing, or behavior change (like in our story).

The use of numerical data guides both the methodology and the analysis protocols. The design also guides and limits how the results are interpreted. Examples of quantitative data found in educational research include test scores, grade point averages, and dropout rates.

experimental research design capstone

Qualitative Research Designs

Qualitative research designs involve obtaining verbal, perspective, and/or visual results using code-based analyses of collected data. Typical qualitative designs used in educational research include the case study, phenomenological, grounded theory, and ethnography. These designs involve exploring behaviors, perceptions/feelings, and social/cultural phenomena found in educational settings.

Qualitative designs result in a written description of the findings. Data collection strategies include observations, interviews, focus groups, surveys, and documentation reviews. The data are recorded as words, phrases, sentences, and paragraphs. Data are then grouped together to form themes. The process of grouping data to form themes is called coding. The labeled themes become the “code” used to interpret the data. The coding can be determined ahead of time before data are collected, or the coding emerges from the collected data. Data collection strategies often include media such as video and audio recordings. These recordings are transcribed into words to allow for the coding analysis.

The use of qualitative data guides both the methodology and the analysis protocols. The “squishy” nature of qualitative data (words vs. numbers) and the data coding analysis limits the interpretation and conclusions made from the results. It is important to explain the coding analysis used to provide clear reasoning for the themes and how these relate to the research questions.

experimental research design capstone

Mixed Method Designs

Mixed Methods research designs are used when the research questions must be answered with results that are both quantitative and qualitative. These designs integrate the data results to arrive at conclusions. A mixed method design is used when there are greater benefits to using multiple data types, sources, and analyses. Examples of typical mixed methods design approaches in education include convergent, explanatory, exploratory, and embedded designs. Using mixed methods approaches in educational research allows the researcher to triangulate, complement, or expand understanding using multiple types of data.

The use of mixed methods data guides the methodology, analysis, and interpretation of the results. Using both qualitative (quant) and quantitative (qual) data analyses provides a clearer or more balanced picture of the results. Data are analyzed sequentially or concurrently depending on the design. While the quantitative and qualitative data are analyzed independently, the results are interpreted integratively. The findings are a synthesis of the quantitative and qualitative analyses.

experimental research design capstone

Applied Research Designs

Applied research designs seek both quantitative and qualitative results to address issues of educational practice. Applied research designs include evaluation, design and development, and action research. The purposes of applied research are to identify best practices, to innovate or improve current practices or policies, to test pedagogy, and to evaluate effectiveness. The results of applied research designs provide practical solutions to problems in educational practice.

Applied designs use both theoretical and empirical data. Theoretical data are collected from published theories or other research. Empirical data are obtained by conducting a needs assessment or other data collection methods. Data analyses include both quantitative and qualitative procedures. The findings are interpreted integratively as in mixed methods approaches, and then “applied” to the problem to form a solution.

experimental research design capstone

Telling the research story

The Research Design in a research project tells the story of what direction the plot of the story will take.  The writing in this heading sets the stage for the rising action of the plot in the research story. The Research Design describes the journey that is about to take place. It functions to guide the reader in understanding the type of path the story will follow. The Research Design is the overall direction of the research story and is determined before deciding on the specific steps to take in obtaining and analyzing the data.

The Research Design heading appears in Chapter 2 of a capstone project. In the capstone project, the Research Design explains the type of design used for conducting the needs assessment.

experimental research design capstone

Capstone Projects in Education: Learning the Research Story Copyright © 2023 by Kimberly Chappell and Greg I. Voykhansky is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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The MEMS MS Capstone at Duke University

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Our Vision: Open-Sourcing Engineering Education

Duke University is deeply committed to social change whereby education is considered the great equalizer that will improve people’s lives.

The Graduate Capstone course ‘ Experiment Design and Research Methods ‘ is a  hallmark of the master’s experience in Duke MEMS.  This  MS curricular requirement engages students from the moment they set foot on campus (both physically and remotely!) in an immersive design-thinking project that they select, define, and address.  MEng students are encouraged to take this course, though not it is not required.  Note that our PhD students can potentially use this course for their qualifying exam requirement.

The Department of Mechanical Engineering and Materials Science  ( MEMS )  is re-imagining the laboratory experience for all engineering students worldwide.  Through hands-on experiments that our graduate students design and build themselves, we are changing the way people are educated.

After their first semester with us, every MS student will be trained in skills that have been identified as critical in their academic success at Duke and as practicing engineers and researchers in the years to come.  In addition, the projects they work on in this course can then be defended at the end-of-semester Graduate Expo, thereby fulfilling their non-thesis MS degree project requirement.

Current Students and Applicants: To learn more about the Master’s programs in MEMS, visit the Department website .  The Graduate Student Portal provides additional guidance for all current MS and MEng students in MEMS. 

This website takes you on a journey through the design process of each experiment that our Master’s students identified themselves.  They were tasked with rethinking hands-on learning activities that would:

  • be cost-effective and composed of easily-accessible parts,
  • contain scaffolded learning modules for undergraduate and graduate students,
  • develop the skills they would need to perform graduate research in the field of their choice,
  • include course content, lectures, sample work, and handouts,
  • cover a small footprint and are quickly transportable, and
  • be free to download.

Watch these Videos to Learn More about our Course and Site

Learn why this course was developed, how our curriculum is unique , and how we personalize every student’s education.

This website was designed specifically by our students with you in mind.

Find out who would most benefit from this website in this video.

Ready to take the plunge?

Watch this video to learn tips and tricks to get the most out of your experience.

Get to know Prof Delagrammatikas’ teaching and research interests here.

Learn More About This Unique Course

Description: Students define a hands-on project of their choice that would prepare them for research in their preferred master’s study concentration.  The design process is employed to identify a problem, formulate it, propose design alternatives and rank them, produce and test prototypes, refine and iterate on the selected design.

The experiments that are developed are incorporated within the graduate and undergraduate curriculum.  The course is composed of equal parts design, technical content, and communication skills (including an online portfolio). Projects can be further developed during the subsequent semester to fulfill the master’s project requirement during the Poster Expo.

  • Prerequisites: Graduate standing or second semester in 4+1 Program
  • Required course for MEMS MS students
  • Strongly encouraged for MEMS MEng students

Objectives:  After taking this course, students will be able to:

  • apply the engineering design process to an open-ended problem, including iterative prototyping,
  • develop teamwork skills through project management,
  • communicate effectively through oral and written means,
  • apply trouble-shooting to physical and virtual designs, perform trade-off analyses (cost-benefit, safety, ethics, etc),
  • and create an online portfolio for professional social media

A general outline of the weekly topics covered in this course are as follows:

experimental research design capstone

Feel free to review the extended syllabus here:

ME555-Capstone-Syllabus-Extended-2021 .

In Spring 2020, MEMS embarked on an ambitious journey to reinvent personalized education.  Rooted deeply in the graduate capstone courses that Prof Sophia Santillan and Prof Nico Hotz developed the previous academic year, this course was developed to further enhance student learning.   Prof George Delagrammatikas joined this course in Spring 2020 to assist in the development of hands-on experiences for the students while also bringing team-teaching to the course.

Much like many schools, we are putting our coursework online for you to use at your convenience.  The difference at Duke is that we will show you how to build your own laboratory, give you a guided tour through our curriculum, and show you what it means to be a Duke mechanical engineer.

Consistent with the Pratt School of Engineering’s motto, our students are ‘ Outrageously Ambitious ’ and take on projects that are challenging, inspiring, and are centered on making the world a better place for society as a whole.

We embrace and celebrate diverse ways of thinking about problems , give the students a voice in defining the types of projects they perform, and always include classmates’ feedback in the problem-solving process.  Students present their work every week and are required to keep a journal of their work.  This website distills their project work for all to learn.

A founding premise behind this course was to develop accessible learning materials for those everywhere, regardless of socioeconomic background, demographics, age, or abilities.

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Guide to the ALM Capstone Project


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There are a number of ways to conduct research for your capstone topic, but everyone must start with the literature review in order to learn what has already been published on your topic. The literature review also helps you identify the different research methods used by scholars in the field that have already produced valid and reliable results.

Indeed, the literature review is the very first step and it is begun when you are crafting your capstone proposal. It is the only way to choose a topic and write your background and research methods section for the proposal. Of course, you'll continue to consult published work during the capstone course as well. Because this step is so important, we've created entire section on this topic (please see Literature Review , under Choosing a Topic).

Commonly used methods:

Case studies. Case studies are in-depth investigations of a single individual (noteworthy museum leader), a group (education department), or event (exhibit). Reading prior case studies is a must to inform your design. Reading case studies may also lead you to museum professionals who authored the published work. These experts could become research participants. Case study is a formal research method with a specific structure. For an introduction, visit Basics of Developing a Case Study from the Free Management Library.

Interviews . You may want to conduct interviews with experts in the field on a specific topic, such as, increasing diversity in musuem membership. Museum professionals have a wealth of information and are ordinarily happy to support beginning scholars. Your capstone reader and instructor can help make introductions through their own networks. Interviews are not simple tasks. You'll need to learn how to conduct interviews in such as way that avoids bias and elicits valid data that can be used for analysis. For an introduction, visit General Guidelines for Conducting Research Interviews from the Free Management Library.

Surveys . Conducting a survey is another way to gather research on your topic. Ordinarily, this method is chosen when you want to gather information from a large data set. Survey design is also not a straightforward task. For an introduction, visit the Harvard University Program on Survey Research.

Program evaluation . You may also consider in-depth and detailed evaluation of an aspect of a specific museum's operation, such as an exhibit or educational programs to understand if and how it met its intended goals. For an introduction, visit Evaluation Activities in Organization from the Free Management Library and Evaluations from the Institute of Museum and Library Resources.

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Design a Capstone Experience

Designing IDEAL capstone experiences is important for promoting the learning and development of all students and for setting them up for success beyond college.

A capstone experience is the culmination of a student's study in a particular major. A capstone experience at Stanford  encourages students to “integrate knowledge and skills developed in the major and to learn and think independently with the tools of the discipline.” Examples include an honors thesis, senior paper or project, and capstone seminar with individual student projects.

Capstones are considered a high-impact educational practice and have been widely shown to be of integral importance to students’ learning and mastery of course material in a major. They require students to assume agency over their learning, synthesize diverse perspectives, respond to targeted feedback, and approximate the methods and outcomes of experts in authentic, real-world contexts. At the same time, students might come to a capstone experience with varying prior experiences, as well as varying future interests and pathways.

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Steps to consider

In pre-capstone courses: 

  • Integrate skills that will be highlighted in the capstone course, such as literature review and synthesis, data collection, recommendations of finding to real world settings (you can use VALUE rubric s as a guide) 
  • Use the learning goals established for the capstone course to help individual courses integrate goals earlier in the coursework (Stanford Teaching Commons Learning Outcomes Guide )

Stanford examples and resources

  • Designing Capstone Experiences , from CTL.
  • Bioengineering capstone
  • Human Biology capstone
  • Religious Studies capstone
  • Urban Studies capstone

The VPUE website on Designing Capstones

Evidence-based Capstone Principles and the Capstone Curriculum website  Guidelines for teachers  published by the Australian Government Office for Teaching and Learning

Reynolds, Julie, Smith, Robin, Moskovitz, Cary, Sayle, Amy (2009). “ BioTAP: A systematic Approach to Teaching Scientific Writing and Evaluating Undergraduate Theses ”, Bioscience , 59(10), 896-903. 

Howe, Susannah, Goldberg, Jay (2019). “ Engineering Capstone Design Education: Current Practices, Emerging Trends, and Successful Strategies ”, In Design Education Today . 

Morreale, Joseph C., Shostya, Anna (2020). “ Creating Transformative Learning Experience Through a Capstone Course in Economics ”, International Review of Economics Education , 35, 100198.

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Study/Experimental/Research Design: Much More Than Statistics

Kenneth l. knight.

Brigham Young University, Provo, UT

The purpose of study, experimental, or research design in scientific manuscripts has changed significantly over the years. It has evolved from an explanation of the design of the experiment (ie, data gathering or acquisition) to an explanation of the statistical analysis. This practice makes “Methods” sections hard to read and understand.

To clarify the difference between study design and statistical analysis, to show the advantages of a properly written study design on article comprehension, and to encourage authors to correctly describe study designs.


The role of study design is explored from the introduction of the concept by Fisher through modern-day scientists and the AMA Manual of Style . At one time, when experiments were simpler, the study design and statistical design were identical or very similar. With the complex research that is common today, which often includes manipulating variables to create new variables and the multiple (and different) analyses of a single data set, data collection is very different than statistical design. Thus, both a study design and a statistical design are necessary.


Scientific manuscripts will be much easier to read and comprehend. A proper experimental design serves as a road map to the study methods, helping readers to understand more clearly how the data were obtained and, therefore, assisting them in properly analyzing the results.

Study, experimental, or research design is the backbone of good research. It directs the experiment by orchestrating data collection, defines the statistical analysis of the resultant data, and guides the interpretation of the results. When properly described in the written report of the experiment, it serves as a road map to readers, 1 helping them negotiate the “Methods” section, and, thus, it improves the clarity of communication between authors and readers.

A growing trend is to equate study design with only the statistical analysis of the data. The design statement typically is placed at the end of the “Methods” section as a subsection called “Experimental Design” or as part of a subsection called “Data Analysis.” This placement, however, equates experimental design and statistical analysis, minimizing the effect of experimental design on the planning and reporting of an experiment. This linkage is inappropriate, because some of the elements of the study design that should be described at the beginning of the “Methods” section are instead placed in the “Statistical Analysis” section or, worse, are absent from the manuscript entirely.

Have you ever interrupted your reading of the “Methods” to sketch out the variables in the margins of the paper as you attempt to understand how they all fit together? Or have you jumped back and forth from the early paragraphs of the “Methods” section to the “Statistics” section to try to understand which variables were collected and when? These efforts would be unnecessary if a road map at the beginning of the “Methods” section outlined how the independent variables were related, which dependent variables were measured, and when they were measured. When they were measured is especially important if the variables used in the statistical analysis were a subset of the measured variables or were computed from measured variables (such as change scores).

The purpose of this Communications article is to clarify the purpose and placement of study design elements in an experimental manuscript. Adopting these ideas may improve your science and surely will enhance the communication of that science. These ideas will make experimental manuscripts easier to read and understand and, therefore, will allow them to become part of readers' clinical decision making.


The terms study design, experimental design, and research design are often thought to be synonymous and are sometimes used interchangeably in a single paper. Avoid doing so. Use the term that is preferred by the style manual of the journal for which you are writing. Study design is the preferred term in the AMA Manual of Style , 2 so I will use it here.

A study design is the architecture of an experimental study 3 and a description of how the study was conducted, 4 including all elements of how the data were obtained. 5 The study design should be the first subsection of the “Methods” section in an experimental manuscript (see the Table ). “Statistical Design” or, preferably, “Statistical Analysis” or “Data Analysis” should be the last subsection of the “Methods” section.

Table. Elements of a “Methods” Section

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Object name is i1062-6050-45-1-98-t01.jpg

The “Study Design” subsection describes how the variables and participants interacted. It begins with a general statement of how the study was conducted (eg, crossover trials, parallel, or observational study). 2 The second element, which usually begins with the second sentence, details the number of independent variables or factors, the levels of each variable, and their names. A shorthand way of doing so is with a statement such as “A 2 × 4 × 8 factorial guided data collection.” This tells us that there were 3 independent variables (factors), with 2 levels of the first factor, 4 levels of the second factor, and 8 levels of the third factor. Following is a sentence that names the levels of each factor: for example, “The independent variables were sex (male or female), training program (eg, walking, running, weight lifting, or plyometrics), and time (2, 4, 6, 8, 10, 15, 20, or 30 weeks).” Such an approach clearly outlines for readers how the various procedures fit into the overall structure and, therefore, enhances their understanding of how the data were collected. Thus, the design statement is a road map of the methods.

The dependent (or measurement or outcome) variables are then named. Details of how they were measured are not given at this point in the manuscript but are explained later in the “Instruments” and “Procedures” subsections.

Next is a paragraph detailing who the participants were and how they were selected, placed into groups, and assigned to a particular treatment order, if the experiment was a repeated-measures design. And although not a part of the design per se, a statement about obtaining written informed consent from participants and institutional review board approval is usually included in this subsection.

The nuts and bolts of the “Methods” section follow, including such things as equipment, materials, protocols, etc. These are beyond the scope of this commentary, however, and so will not be discussed.

The last part of the “Methods” section and last part of the “Study Design” section is the “Data Analysis” subsection. It begins with an explanation of any data manipulation, such as how data were combined or how new variables (eg, ratios or differences between collected variables) were calculated. Next, readers are told of the statistical measures used to analyze the data, such as a mixed 2 × 4 × 8 analysis of variance (ANOVA) with 2 between-groups factors (sex and training program) and 1 within-groups factor (time of measurement). Researchers should state and reference the statistical package and procedure(s) within the package used to compute the statistics. (Various statistical packages perform analyses slightly differently, so it is important to know the package and specific procedure used.) This detail allows readers to judge the appropriateness of the statistical measures and the conclusions drawn from the data.


Avoid using the term statistical design . Statistical methods are only part of the overall design. The term gives too much emphasis to the statistics, which are important, but only one of many tools used in interpreting data and only part of the study design:

The most important issues in biostatistics are not expressed with statistical procedures. The issues are inherently scientific, rather than purely statistical, and relate to the architectural design of the research, not the numbers with which the data are cited and interpreted. 6

Stated another way, “The justification for the analysis lies not in the data collected but in the manner in which the data were collected.” 3 “Without the solid foundation of a good design, the edifice of statistical analysis is unsafe.” 7 (pp4–5)

The intertwining of study design and statistical analysis may have been caused (unintentionally) by R.A. Fisher, “… a genius who almost single-handedly created the foundations for modern statistical science.” 8 Most research did not involve statistics until Fisher invented the concepts and procedures of ANOVA (in 1921) 9 , 10 and experimental design (in 1935). 11 His books became standard references for scientists in many disciplines. As a result, many ANOVA books were titled Experimental Design (see, for example, Edwards 12 ), and ANOVA courses taught in psychology and education departments included the words experimental design in their course titles.

Before the widespread use of computers to analyze data, designs were much simpler, and often there was little difference between study design and statistical analysis. So combining the 2 elements did not cause serious problems. This is no longer true, however, for 3 reasons: (1) Research studies are becoming more complex, with multiple independent and dependent variables. The procedures sections of these complex studies can be difficult to understand if your only reference point is the statistical analysis and design. (2) Dependent variables are frequently measured at different times. (3) How the data were collected is often not directly correlated with the statistical design.

For example, assume the goal is to determine the strength gain in novice and experienced athletes as a result of 3 strength training programs. Rate of change in strength is not a measurable variable; rather, it is calculated from strength measurements taken at various time intervals during the training. So the study design would be a 2 × 2 × 3 factorial with independent variables of time (pretest or posttest), experience (novice or advanced), and training (isokinetic, isotonic, or isometric) and a dependent variable of strength. The statistical design , however, would be a 2 × 3 factorial with independent variables of experience (novice or advanced) and training (isokinetic, isotonic, or isometric) and a dependent variable of strength gain. Note that data were collected according to a 3-factor design but were analyzed according to a 2-factor design and that the dependent variables were different. So a single design statement, usually a statistical design statement, would not communicate which data were collected or how. Readers would be left to figure out on their own how the data were collected.


With the advent of electronic data gathering and computerized data handling and analysis, research projects have increased in complexity. Many projects involve multiple dependent variables measured at different times, and, therefore, multiple design statements may be needed for both data collection and statistical analysis. Consider, for example, a study of the effects of heat and cold on neural inhibition. The variables of H max and M max are measured 3 times each: before, immediately after, and 30 minutes after a 20-minute treatment with heat or cold. Muscle temperature might be measured each minute before, during, and after the treatment. Although the minute-by-minute data are important for graphing temperature fluctuations during the procedure, only 3 temperatures (time 0, time 20, and time 50) are used for statistical analysis. A single dependent variable H max :M max ratio is computed to illustrate neural inhibition. Again, a single statistical design statement would tell little about how the data were obtained. And in this example, separate design statements would be needed for temperature measurement and H max :M max measurements.

As stated earlier, drawing conclusions from the data depends more on how the data were measured than on how they were analyzed. 3 , 6 , 7 , 13 So a single study design statement (or multiple such statements) at the beginning of the “Methods” section acts as a road map to the study and, thus, increases scientists' and readers' comprehension of how the experiment was conducted (ie, how the data were collected). Appropriate study design statements also increase the accuracy of conclusions drawn from the study.


The goal of scientific writing, or any writing, for that matter, is to communicate information. Including 2 design statements or subsections in scientific papers—one to explain how the data were collected and another to explain how they were statistically analyzed—will improve the clarity of communication and bring praise from readers. To summarize:

  • Purge from your thoughts and vocabulary the idea that experimental design and statistical design are synonymous.
  • Study or experimental design plays a much broader role than simply defining and directing the statistical analysis of an experiment.
  • A properly written study design serves as a road map to the “Methods” section of an experiment and, therefore, improves communication with the reader.
  • Study design should include a description of the type of design used, each factor (and each level) involved in the experiment, and the time at which each measurement was made.
  • Clarify when the variables involved in data collection and data analysis are different, such as when data analysis involves only a subset of a collected variable or a resultant variable from the mathematical manipulation of 2 or more collected variables.


Thanks to Thomas A. Cappaert, PhD, ATC, CSCS, CSE, for suggesting the link between R.A. Fisher and the melding of the concepts of research design and statistics.

Capstone Project

Capstone Project

The Capstone Project is a cornerstone of our program, offering students the chance to deeply engage with translational research topics they're passionate about. This endeavor spans the spectrum of therapeutics and diagnostics, including areas like drug therapy, vaccines, and gene therapy. It covers a wide array of research stages, from initial clinical translation to real-world application.

Time Commitment : Starting in the Winter Quarter and continuing through August 2025, students should plan for a minimum of 10 hours weekly on their project, with many dedicating more time to meet their objectives.

Projects may be laboratory-based (wet-lab or computational lab) or focus on clinical trials or regulatory aspects of translation. The culmination of the program includes a poster presentation and quarterly product development plan presentations. Unlike a thesis master's, the capstone emphasizes skill and knowledge application within a clinical context, supported by faculty mentorship and industry guidance.

Capstone Project Requirements

Areas of focus: Capstone projects should focus on therapeutics and/or diagnostics involving drug therapy and delivery, vaccines, immune measurements and therapy, or gene measurements and therapy, and can include a range of translational research activities from early-stage clinical translation (T0/T1) to preclinical optimization and validation (T2) to clinical validation and integration (T3) to implementation and dissemination in real-world settings (T4). The program is designed to equip students with the skills and knowledge necessary to navigate the complex and dynamic landscape of biomedical innovation and translation.

The capstone research project typically takes place within Stanford faculty research labs. However, working professionals (students who are already employed at local drug or biotech companies) have the option to conduct their capstone within their respective companies, benefiting from industry and academic mentorship.

Initiating a project:

Before officially starting M-TRAM studies in the Fall, students engage in in-depth discussions with the M-TRAM leadership team regarding their interests, career aspirations, and potential project concepts (between May to September. Through mutual agreement between the student and M-TRAM, efforts are made to identify and assign the most suitable capstone advisor based on alignment of interests and expertise.  

During the fall quarter, students dedicate time to engaging in thorough discussions with their advisors regarding potential research project ideas. They delve into in-depth reading and exploration of various concepts, aiming to refine and solidify their understanding of their potential projects. This period serves as a crucial phase for students to narrow down their focus and lay the groundwork for their capstone proposals.

By the end of the first quarter, students are required to present their capstone project proposal to the M-TRAM directors and other students in the program.

The proposal and capstone advisor must be approved by the M-TRAM Directors prior to the onset of the project.  

Goals of the capstone project:   The capstone project serves as a bridge between scientific innovation and real-world application, providing students with a hands-on experience in navigating the journey from idea conception to patient delivery. It's essentially an exercise in contextualizing scientific ideas within the broader landscape of healthcare, understanding where it fits in, and devising a strategic development plan for a therapeutic/diagnostic.

Throughout the capstone, students learn how to translate scientific concepts into actionable plans that address unmet medical needs and improve patient outcomes. This involves conducting thorough research to identify the clinical relevance and market potential of their ideas, as well as understanding the regulatory and commercial considerations involved in bringing them to fruition.By engaging in the capstone project, students gain valuable skills in strategic planning, market analysis, and stakeholder communication. They learn how to formulate a development plan that outlines the pathway from concept to commercialization, including key milestones, resource requirements, and risk management strategies.

Overall, the capstone project provides students with a comprehensive understanding of the process of biomedical innovation, equipping them with the knowledge and skills needed to drive meaningful change in healthcare.  

Capstone Committee: At the end of the first quarter, students designate a Capstone faculty advisor, and a technology advisor (this could be scientific mentor, such as a core director or a postdoctoral project mentor).  

Project timeline and progress: The student, M-TRAM directors and the Capstone advisors agree on a proposed timeline for completion. The Committee will review the proposal and offer guidance and monitoring throughout the project. During quarters two through four (Winter, Spring, Summer), students will meet regularly with their capstone advisors to discuss their progress. At the end of each quarter, student will present their progress to the M-TRAM directors and other students.

Capstone completion: Upon completion of the project, students will formally present their final results at the student research showcase in the beginning of September following their graduation. In addition to the poster, students will be required to present their capstone progress at the end of each quarter (December, March and May).

Capstone Project Proposal Guidelines

  • Student will regularly meet with the advisor(s) and M-TRAM leadership to monitor progress of their project and to provide advice and feedback
  • The culmination of the program includes a poster presentation at the M-TRAM Symposium (beginning of September after graduation) and quarterly product development plan presentations.
  • MTRAM will support each student's research with a research stipend of $3,500 (reagents, consumables, kits, services).


  • “ AI/machine learning enabled structure-based drug discovery. ”
  • Capstone advisor: Russ Altman, MD, Ph.D ., Kenneth Fong Professor of Bioengineering, Genetics, Medicine, Biomedical Data Science and (by courtesy) Computer Science), past chairman of the Bioengineering Department
  • “Pharmacological validation of clinically relevant cancer targets “
  • Capstone advisor: Nathanael Gray, MD, Ph.D ., Krishnan Shah Family Professor of Chemical and Systems Biology, Co-Lead of Medicinal Chemistry (IMA: Innovative Medicines Accelerator)


  • “Developing therapeutics for pulmonary arterial hypertension (PAH).”
  • Capstone advisor: Vinicio de Jesus Perez, MD , Associate Professor of Pulmonary and Critical Care Medicine


  • “Tumor infiltrating lymphocyte (TIL) therapy for solid tumors (melanoma)”
  • Capstone advisor: Allison Betof Warner, MD, PhD , Assistant Professor of Medicine (Oncology), Director of the Melanoma Program and Faculty Leader of the Melanoma|Cutaneous Oncology Clinical Research Group in the SCI-Cancer Clinical Trials Office


  • “Glycoproteomics based early cancer detection.”
  • Capstone advisor: Carolyn Bertozzi, PhD , Baker Family Director of Stanford Sarafan ChEM-H, Anne T. and Robert K. Bass Professor, School of Humanities and Sciences
  • Industry collaboration with InterVenn Biosciences (company)


  • "Anti-Myc cancer therapeutics"
  • Capstone advisor: Dean Felsher, MD, PhD , Professor of Medicine (Oncology) and of Pathology, TRAM Director, M-TRAM Faculty Director, Co-Director Cancer Nanotechnology Program, Department of Radiology, Stanford School of Medicine, Director of Admissions/Associate Director, Medical Scientist Training Program, Director of Advanced Residency Training Program, Stanford University School of Medicine, Co-Director of Spectrum KL2 Mentored Development Program, Stanford University, School of Medicine
  • "AI enabled drug discovery for breast cancer"
  • Capstone advisor: Christina Curtis, MD, PhD , Professor of Medicine, Genetics and Biomedical Data Science, Director of Artificial Intelligence and Cancer Genomics, Director - Breast Cancer Translational Research (Stanford Cancer Institute), Co-Director - Molecular Tumor Board, Stanford Cancer Institut


  • "T-reg cell immunotherapy for graft vs. host disease"
  • Capstone advisor: Everett Meyer, MD, Associate Professor of Medicine, Division of Blood & Marrow Transplantation and Cellular Therapy


  • " Developing a therapeutic for SMA (spital muscular atrophy) neurological disorder: computational analysis of clinical trial data"
  • Capstone advisor: Jacinda Sampson, MD, PhD, Clinical Professor of Neurology and Neurological Sciencies


  • "Immunotherapy for IBD (inflammatory bowel disease)"
  • Capstone advisor: Sidhartha Sinha, MD, Assistant Professor of Medicine (Gastroenterology and Hepatology), Director of Digital Health and Innovation, Division of Gastroenterology & Hepatology   


  • "Developing a therapeutic for infectious disease (malaria)"
  • Capstone advisor: Prasanna Jagannathan, MD , Assistant Professor of Medicine (Infectious Diseases) and of Microbiology and Immunology


  • "Drug repurposing for treatment of cardio valve disease"
  • Capstone advisor: Ian Chen, MD , Assistant Professor of Medicine (Cardiovascular Disease) and of Radiology (Veterans Affairs), Director, Translational Cardiovascular Research Laboratory, Veterans Affairs Palo Alto Health Care System, Director, VA/PAVIR Summer Research Program

Capstone Projects 2022-23

Chris aboujudom.

  • “ Development of Novel MYC-directed Anti-cancer Therapeutics ”
  • Capstone advisor: Dean Felsher, MD Ph.D ., Professor of Medicine (Oncology) and of Pathology, M-TRAM Program Director,


  • “Developing protocols for isolation and purification of MYC-derived cancer extracellular vesicles (EVs) for improved diagnosis and monitoring of cancer.“
  • Capstone advisor: Dean Felsher, MD Ph.D ., Professor of Medicine (Oncology) and of Pathology, M-TRAM Program Director


  • “Development of a combined cancer vaccine and immunotherapy (anti-PD-L1) delivery using dendritic cell-based microbubbles against triple-negative breast cancer”
  • Capstone advisor: Ramasamy Paulmurugan, PhD , Professor of Radiology, Molecular Imaging Program at Stanford
  • “Investigate the roles and therapeutic value of human anti-phagocytotic genes in augmenting CAR-T cell therapy”
  • Capstone advisor: Crystal Mackall, MD (Capstone Primary Advisor Faculty Mentor), Founding Director of the Stanford Center for Cancer Cell Therapy, Professor of Pediatrics and Medicine


  • "High-resolution proteomic profiling of aqueous humor liquid biopsies as a diagnostic and prognostic tool for choroidal melanoma"
  • Capstone advisor: Vinit Mahajan, MD, PhD , Professor of Ophthalmology, Vice Chair for Research (Ophthalmology)
  • Capstone advisor: Nima Aghaeepour, PhD , Associate Professor of Anesthesiology, Pediatrics and Biomedical Science

Applications portal is now closed  

For the 2024/2025 academic year, we will be accepting applications for 2025/26, in the fall of 2024..

Questions? Contact us! [email protected]

Important Dates

September 2024 to January 2024:

  • Applications accepted for 2025/26

December, 2024 (date tba):

  • M-TRAM info session webinar for prospective students 

January 15, 2025:

  • Applications are due for 2025/26

April, 2025:

  • Admission Decisions

Sept. 2025: (date tba)

  • M-TRAM research symposium and New Students Orientation (in person) - stay tuned for registration info

Sept. 22, 2025:

  • First day of classes at Stanford (M-TRAM program starts)

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Interested in Becoming an M-TRAM Industry Partner?

We welcome inquiries from biotechnology, pharmaceutical and other health care organizations interested in learning about opportunities to partner with M-TRAM: 

[email protected]

Engineering Capstone Design Education: Current Practices, Emerging Trends, and Successful Strategies

  • First Online: 17 May 2019

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experimental research design capstone

  • Susannah Howe 4 &
  • Jay Goldberg 5  

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Capstone design courses play an important role in the preparation of engineering students for professional practice and career success. They allow students to apply what they have learned prior to their senior year along with newly developed skills relating to the solution of real-world design problems, often in a team environment with external clients. Team-based capstone design courses provide opportunities for students to develop teamwork and communication skills. In the United States, accredited engineering programs are required to offer a major design experience; most programs fulfill this requirement through a capstone design course. Differences in course learning outcomes, management, structure, duration, projects, student teams, and required deliverables exist among institutions. This chapter presents the current state of engineering capstone design education and highlights changes to capstone design practices in the U.S. over the past 25 years, based on extensive, seminal surveys of capstone design programs. It discusses current practices and successful strategies in engineering capstone design education. The chapter includes perspectives and feedback from hundreds of engineering capstone design faculty regarding their personal experiences with capstone design programs. This chapter also provides recommendations for supporting engineering capstone design experiences based on the authors’ vast experience teaching and managing capstone design courses and engaging with the capstone design community. These recommendations include scaffolding the design curriculum, fostering industry involvement in capstone design courses, keeping courses and faculty up-to-date with current design practices, obtaining organizational support for capstone design courses, sourcing capstone design projects, and preparing students for professional practice.

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Howe, S., Goldberg, J. (2019). Engineering Capstone Design Education: Current Practices, Emerging Trends, and Successful Strategies. In: Schaefer, D., Coates, G., Eckert, C. (eds) Design Education Today. Springer, Cham. https://doi.org/10.1007/978-3-030-17134-6_6

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Enago Academy

Experimental Research Design — 6 mistakes you should never make!

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Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

  • When time is an important factor in establishing a relationship between the cause and effect.
  • When there is an invariable or never-changing behavior between the cause and effect.
  • Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

  • One-shot Case Study Research Design
  • One-group Pretest-posttest Research Design
  • Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

  • There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
  • A variable that can be manipulated by the researcher
  • Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

experimental research design

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

  • Researchers have firm control over variables to obtain results.
  • The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
  • The results are specific.
  • Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
  • Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
  • Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

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Research Design Resources

The following links provide definitions and information on research designs. 

  • Study Types & Designs - University of Missouri Health Sciences Library Definitions of study types/designs and tips for understanding research designs and EBP
  • Centre for Evidence Based Medicine - Study Designs A brief guide to the different study types and a comparison of the advantages and disadvantages of the different types of study.
  • University of Alabama at Birmingham - Lister Hill Library of Health Sciences - Top Ten Things You Need to Know About EBP Go to the sections on EBP and Research Fact Sheets to locate tips on EBP.
  • Indiana State University Cunninham Memorial Library - EBP Terminology Definitions related to EBP, including case and cohort studies.
  • American Association of Colleges of Nursing - Evidence Based Practice by Karen N. Drenkard, PhD, RN, NEA-BC, FAAN QSEN Workshop presentation.
  • University of Minnesota Health Sciences Library - Understanding Research Designs Basic definitions and examples of study designs.

Additional Definitions and Sample Articles

Definitons of research designs from Introduction to Evidence Based Practice:  A Practical Guide for Nursing by Lisa Hopp and Leslie Rittenme yer .

Case Controlled studies are where researchers conduct a comparison of cases with a particular outcome and cases without a particular outcome to evaluate the participants’ exposure.  

Case Series/Case Report  is a research design that track patients with a known exposure given similar treatment or examines their medical records for exposure and outcome.  

Cohort studies with a control group are those where a group of people with something in common (a cohort) are followed.   This group is compared to another group with similar characteristics/circumstances, with the exception of the factor being investigated.

Cross-sectional studies involve data collected at a defined time, providing a snapshot of a disease in the population (observational studies).

Meta-analysis uses statistical methods to pool the results of independent studies (quantitative).  Meta-synthesis is a qualitative analysis of a group of individual studies in which the finding of the studies are pooled.

Randomized Clinical Trial is an experiment using human beings in which the investigator randomly assigns participants in the trial either to a treatment or control (no treatment) group.

Systematic Reviews attempt to synthesize and summarize evidence from existing primary studies. They use explicit and transparent methods to include/exclude studies on a topic, and rigorously analyze the results to form a conclusion.

  • Example of Case Control Study
  • Example of Case Report
  • Example of Case Series
  • Example of Cohort Study
  • Example of Cross-Sectional Study
  • Example of Randomized Controlled Trial
  • Example of a Systematic Review
  • << Previous: Appraising the Evidence
  • Next: Levels of Evidence >>
  • Last Updated: May 6, 2024 10:01 AM
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The 2023-2024 MIE Capstone Design Showcase Honours and Awards Innovative Student Projects

The end of term saw fourth-year Mechanical and Industrial Engineering students culminate their year-long Capstone Design projects at the annual MIE Capstone Design Showcase. A total of 80 teams, comprising over 330 students, presented their prototypes, posters, and final recommendations to faculty and industry clients at Hart House. The caliber of projects was outstanding and offered innovative solutions to all of the clients involved with some of those projects receiving awards. The MIE490 and MIE491 award winners are listed below.

experimental research design capstone

Team Safran2 (Samantha Butt, Lydia Callender, Jeremy Mainella and Ana Vukojevic) won the 1st Place Capstone Design Project Award (Mechanical) and John H. Weber Scholarship

1st Place Capstone Design Project Award (Mechanical) and John H. Weber Scholarship

Project Title: Adaptive Landing Gear for Helicopters / Project Supervisor: Professor Matthew Mackay

SAFRAN Landing Systems wanted to design a safer way for rescue helicopters to land on steep terrain and team members Samantha Butt, Lydia Callender, Jeremy Mainella , and Ana Vukojevic delivered. The result is “AeroFlex” – an innovative flexure-based landing gear prototype that is lightweight and adapts to a 20-degree sloped terrain. Rescue helicopters can perform safer and more efficient maneuvers in mountainous terrain in response to the thousands of calls the Canada National Search & Rescue program receives each year.

experimental research design capstone

Team Mott MacDonald (Varun Kamboj, Mika Sustar, Marzuk Khan, and Matin Sarahi) won 1st Place Capstone Design Project Award (Industrial) and Peri Family Industrial Engineering Design Award

1st Place Capstone Design Project Award (Industrial) and Peri Family Industrial Engineering Design Award

Project Title : InfraPOV: Infrastructure Public Opinion Visualizer / Project Supervisor : Professor Scott Sanner

Numerous stakeholders participate in the development of large-scale infrastructure projects. Mott MacDonald wanted to move away from the industry standard of manually evaluating comments and concerns to provide their clients with more accurate data to reflect constituent voices. Enter “InfraPOV: Infrastructure Public Opinion Visualizer” a design by Capstone team Varun Kamboj, Mika Sustar, Marzuk Khan, and Matin Sarahi , which leverages Large Language Model technology to sum up and categorize huge swaths of data from unstructured comments. Time spent analyzing stakeholder data was reduced by 95%, offering Mott MacDonald a more efficient way to extract insights from public opinion and provide clients with meaningful solutions.

2nd Place Capstone Design Project Award (Mechanical & Industrial)

Project Title : Thermal Characterization and Simulation Framework of Large-Format Electric Vehicle Lithium-Ion Batteries / Project Supervisor : Professor Cristina Amon

Lithium-ion battery performance and safety can be negatively affected by the heat generated during charging and discharging. The ATOMS Laboratory team produced an innovative experimental approach to characterize and enhance the thermal performances of novel battery systems. Team members Daniel Lee, John Abellanoza, Noah D’Emilio, and Mitchell Chong , designed and prototyped several cell test rigs and accurately incorporated realistic operating conditions of battery cells in commercial electric vehicles (EVs) and stationary battery energy storage systems (BESS). The team added I-shaped cooling fins to the battery module design to create more equal temperature distribution across the cell and lowering overall temperature by 10% to enable faster charging.

Project Title : Modelling and Evaluation of a Tier-Based System for Grandview Kids Children’s Treatment Centres / Project Supervisor : Professor Vahid Sarhangian

Reducing rehabilitation wait times is a priority for Grandview Kids (GVK) , a Children’s Treatment Centre. Using simulation modeling, the Capstone team of Max Beggs, Claire Shaw, and Jose Pablo Siliézar analyzed the effects of a tiered intervention system to improve patient flow. Moving away from the current non-tiered system of assessments and 1-on-1 appointments, the team constructed a simulation model in Python to include Tier 1 Workshops and Tier 2 Group Therapy interventions to provide temporary treatment options to patients. This tiered system was found to decrease queue sizes and wait times by up to 20% and provided evidence-based recommendations to enhance patient flow management and provide equitable access to care for children with developmental delays.

3rd Place Capstone Design Project Award (Mechanical & Industrial)

Project Title : Experimental Methodology For Measuring Propeller Noise / Project Supervisor : Professor Kamran Behdinan

Drones in aviation are noisy and as they are used more frequently, the increase in noise levels is cause for concern. Tiffany Costas, Daniel Roberts, Adli Hijab, Peter James Mason, and Nicholas Bajaikine worked with their ARL-MLS Laboratory client to find a solution. An optimal propeller design is a promising way to mitigate noise pollution and collecting noise data to determine efficacy was the team’s objective. The result was a testing apparatus prototype that measured detailed and flexible propeller noise characterization from many different propeller geometries. This final product offers the flexibility of rapid testing within the lab at a lower cost than the current market alternatives.

Project Title : Emergency Department Resource & Scheduling Allocation to Optimise Efficiency / Project Supervisor : Professor Michael Carter

Project client Humber River Health (HRH) has the busiest Emergency Department (ED) in Ontario and wants to find ways to optimize patient flow and reduce wait times. Team members Andrew Barton, Emma Beaumount, Maia Kanceljak, and Alexandra Hon used simulation technology and data analytics to find solutions. Following an ambulatory patient’s journey until seen by a physician, the team determined that reallocating nurse staffing numbers at different shift stages would reduce time-to-physician initial assessments (PIA), and minimize overall wait times.

-Published by Kendra Hunter on May 16, 2024

Department of Mechanical & Industrial Engineering University of Toronto 5 King’s College Road Toronto, Ontario • M5S 3G8 • Canada Phone: +1-416-978-3040

Traditional Land Acknowledgement

We wish to acknowledge this land on which the University of Toronto operates. For thousands of years it has been the traditional land of the Huron-Wendat, the Seneca, and the Mississaugas of the Credit. Today, this meeting place is still the home to many Indigenous people from across Turtle Island and we are grateful to have the opportunity to work on this land.

© 2024 Faculty of Applied Science & Engineering

Texas Tech Now

Engineering capstone project yields useful tool.

May 17, 2024

Engineering Capstone Project Yields Useful Tool

A piece of research equipment made by students will be used in the Edward E. Whitacre Jr. College of Engineering.

What started as a capstone project handed to a group of students in Texas Tech University 's Edward E. Whitacre Jr. College of Engineering has yielded a useful tool for future research. 

Given the opportunity to select a project, the undergraduate students in the Department of Mechanical Engineering leaned into helping current and future graduate students by creating a tool that could be used in rocket research.  

The equipment built by the students is called a thrust stand, and if you're not exactly sure what that is, you're not alone. 

“Basically, a thrust stand is a mounted rocket motor,” explained Joseph Pantoya, one of the mechanical engineering students involved in the project. “It collects thrust and pressure data for a given rocket fuel. 

“What we can do is get fuels that the combustion lab makes, put them in our rocket motor and test them out in a controlled environment.”

The capstone course brought together a team of six students from diverse backgrounds to complete the final steps in their mechanical engineering degrees with their project supported by grants from the U.S. Department of Energy (DOE) and the U.S. Department of Defense (DOD).

Grants from sources like DOE and DOD give professors the resources needed to supply students with a hands-on learning experience while also creating something of value for the wider world. 

In this case, the thrust stand will be used by both graduate and undergraduate students in the Combustion Lab , where testing of accelerants used in various types of rockets takes place daily. The capstone project will help researchers test solid fuel combustion and better understand how those fuels can be designed to advance hypersonic combustion for propulsion applications. 

“Being able to help students in the lab publish research papers one day with something we designed is really cool,” said Juan Aguirre, another of the students involved with the thrust stand project. 

Engineering Studets

The project required working with graduate students in the lab to understand and address their needs in the design phase. Meeting the needs of those students was a critical piece of the puzzle, but it wasn't the only piece. 

Moving from the theoretical aspects of design into the actual production phase, managing a budget and producing a useful final product were all hurdles the thrust stand team had to conquer.

“We had a lot of challenges,” team member Ajibek Karatalov said. “Most of the challenges were logistical. For example, one of the main parts was shipped from Japan, and it never made it. I don't know why. So, these kinds of challenges were sort of boundaries, but I'm glad that we overcame them as a team.”

Luckily for the students, there are plenty of resources and mentors to lean on. Mechanical engineering's machine shop, for instance, provided the expertise the students needed to work through many of the technical issues along with the sage advice that comes from working with professionals. 

“I think having the shop instructors, Roy Mullins and David Meyers , they kind of gave us a new perspective on the issues we were facing,” said Jeffery (Mitch) McHugh, another team member. “They had more of a rounded perspective because they've worked in the field. That really helped us and gave us a perspective of what people that we may be working with in the future will have to say.”

Students standing in ballroom at Mackenzie Merket Alumni Center

The team's design was on display at the Mechanical Engineering Expo, an event held on Texas Tech's campus where, along with other teams from the department, the work of the last year is shown off to the campus community.

Mullins and the staff at the machine shop work with a wide range of students daily, helping with things like welding and machining parts that wouldn't normally be done by engineers in the field, and he was impressed with the thrust stand team's competency. 

“They've been a pretty self-sufficient group, actually,” Mullins said. “We've had to answer the usual technical questions and assist them in some machining, but for the most part, with the end design they've done really well on their own.

“It was a very specific subject. It was a research project tied to research we do in the department, so that was kind of unique in and of itself. But what really struck us about this project was it was for a research project that ties immediately to a critical problem.”

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