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A systematic review of research on online teaching and learning from 2009 to 2018

Associated data.

Systematic reviews were conducted in the nineties and early 2000's on online learning research. However, there is no review examining the broader aspect of research themes in online learning in the last decade. This systematic review addresses this gap by examining 619 research articles on online learning published in twelve journals in the last decade. These studies were examined for publication trends and patterns, research themes, research methods, and research settings and compared with the research themes from the previous decades. While there has been a slight decrease in the number of studies on online learning in 2015 and 2016, it has then continued to increase in 2017 and 2018. The majority of the studies were quantitative in nature and were examined in higher education. Online learning research was categorized into twelve themes and a framework across learner, course and instructor, and organizational levels was developed. Online learner characteristics and online engagement were examined in a high number of studies and were consistent with three of the prior systematic reviews. However, there is still a need for more research on organization level topics such as leadership, policy, and management and access, culture, equity, inclusion, and ethics and also on online instructor characteristics.

• • Twelve online learning research themes were identified in 2009–2018.
• • A framework with learner, course and instructor, and organizational levels was used.
• • Online learner characteristics and engagement were the mostly examined themes.
• • The majority of the studies used quantitative research methods and in higher education.
• • There is a need for more research on organization level topics.

1. Introduction

Online learning has been on the increase in the last two decades. In the United States, though higher education enrollment has declined, online learning enrollment in public institutions has continued to increase ( Allen & Seaman, 2017 ), and so has the research on online learning. There have been review studies conducted on specific areas on online learning such as innovations in online learning strategies ( Davis et al., 2018 ), empirical MOOC literature ( Liyanagunawardena et al., 2013 ; Veletsianos & Shepherdson, 2016 ; Zhu et al., 2018 ), quality in online education ( Esfijani, 2018 ), accessibility in online higher education ( Lee, 2017 ), synchronous online learning ( Martin et al., 2017 ), K-12 preparation for online teaching ( Moore-Adams et al., 2016 ), polychronicity in online learning ( Capdeferro et al., 2014 ), meaningful learning research in elearning and online learning environments ( Tsai, Shen, & Chiang, 2013 ), problem-based learning in elearning and online learning environments ( Tsai & Chiang, 2013 ), asynchronous online discussions ( Thomas, 2013 ), self-regulated learning in online learning environments ( Tsai, Shen, & Fan, 2013 ), game-based learning in online learning environments ( Tsai & Fan, 2013 ), and online course dropout ( Lee & Choi, 2011 ). While there have been review studies conducted on specific online learning topics, very few studies have been conducted on the broader aspect of online learning examining research themes.

2. Systematic Reviews of Distance Education and Online Learning Research

Distance education has evolved from offline to online settings with the access to internet and COVID-19 has made online learning the common delivery method across the world. Tallent-Runnels et al. (2006) reviewed research late 1990's to early 2000's, Berge and Mrozowski (2001) reviewed research 1990 to 1999, and Zawacki-Richter et al. (2009) reviewed research in 2000–2008 on distance education and online learning. Table 1 shows the research themes from previous systematic reviews on online learning research. There are some themes that re-occur in the various reviews, and there are also new themes that emerge. Though there have been reviews conducted in the nineties and early 2000's, there is no review examining the broader aspect of research themes in online learning in the last decade. Hence, the need for this systematic review which informs the research themes in online learning from 2009 to 2018. In the following sections, we review these systematic review studies in detail.

Comparison of online learning research themes from previous studies.

2.1. Distance education research themes, 1990 to 1999 ( Berge & Mrozowski, 2001 )

Berge and Mrozowski (2001) reviewed 890 research articles and dissertation abstracts on distance education from 1990 to 1999. The four distance education journals chosen by the authors to represent distance education included, American Journal of Distance Education, Distance Education, Open Learning, and the Journal of Distance Education. This review overlapped in the dates of the Tallent-Runnels et al. (2006) study. Berge and Mrozowski (2001) categorized the articles according to Sherry's (1996) ten themes of research issues in distance education: redefining roles of instructor and students, technologies used, issues of design, strategies to stimulate learning, learner characteristics and support, issues related to operating and policies and administration, access and equity, and costs and benefits.

In the Berge and Mrozowski (2001) study, more than 100 studies focused on each of the three themes: (1) design issues, (2) learner characteristics, and (3) strategies to increase interactivity and active learning. By design issues, the authors focused on instructional systems design and focused on topics such as content requirement, technical constraints, interactivity, and feedback. The next theme, strategies to increase interactivity and active learning, were closely related to design issues and focused on students’ modes of learning. Learner characteristics focused on accommodating various learning styles through customized instructional theory. Less than 50 studies focused on the three least examined themes: (1) cost-benefit tradeoffs, (2) equity and accessibility, and (3) learner support. Cost-benefit trade-offs focused on the implementation costs of distance education based on school characteristics. Equity and accessibility focused on the equity of access to distance education systems. Learner support included topics such as teacher to teacher support as well as teacher to student support.

2.2. Online learning research themes, 1993 to 2004 ( Tallent-Runnels et al., 2006 )

Tallent-Runnels et al. (2006) reviewed research on online instruction from 1993 to 2004. They reviewed 76 articles focused on online learning by searching five databases, ERIC, PsycINFO, ContentFirst, Education Abstracts, and WilsonSelect. Tallent-Runnels et al. (2006) categorized research into four themes, (1) course environment, (2) learners' outcomes, (3) learners’ characteristics, and (4) institutional and administrative factors. The first theme that the authors describe as course environment ( n  = 41, 53.9%) is an overarching theme that includes classroom culture, structural assistance, success factors, online interaction, and evaluation.

Tallent-Runnels et al. (2006) for their second theme found that studies focused on questions involving the process of teaching and learning and methods to explore cognitive and affective learner outcomes ( n  = 29, 38.2%). The authors stated that they found the research designs flawed and lacked rigor. However, the literature comparing traditional and online classrooms found both delivery systems to be adequate. Another research theme focused on learners’ characteristics ( n  = 12, 15.8%) and the synergy of learners, design of the online course, and system of delivery. Research findings revealed that online learners were mainly non-traditional, Caucasian, had different learning styles, and were highly motivated to learn. The final theme that they reported was institutional and administrative factors (n  = 13, 17.1%) on online learning. Their findings revealed that there was a lack of scholarly research in this area and most institutions did not have formal policies in place for course development as well as faculty and student support in training and evaluation. Their research confirmed that when universities offered online courses, it improved student enrollment numbers.

2.3. Distance education research themes 2000 to 2008 ( Zawacki-Richter et al., 2009 )

Zawacki-Richter et al. (2009) reviewed 695 articles on distance education from 2000 to 2008 using the Delphi method for consensus in identifying areas and classified the literature from five prominent journals. The five journals selected due to their wide scope in research in distance education included Open Learning, Distance Education, American Journal of Distance Education, the Journal of Distance Education, and the International Review of Research in Open and Distributed Learning. The reviewers examined the main focus of research and identified gaps in distance education research in this review.

Zawacki-Richter et al. (2009) classified the studies into macro, meso and micro levels focusing on 15 areas of research. The five areas of the macro-level addressed: (1) access, equity and ethics to deliver distance education for developing nations and the role of various technologies to narrow the digital divide, (2) teaching and learning drivers, markets, and professional development in the global context, (3) distance delivery systems and institutional partnerships and programs and impact of hybrid modes of delivery, (4) theoretical frameworks and models for instruction, knowledge building, and learner interactions in distance education practice, and (5) the types of preferred research methodologies. The meso-level focused on seven areas that involve: (1) management and organization for sustaining distance education programs, (2) examining financial aspects of developing and implementing online programs, (3) the challenges and benefits of new technologies for teaching and learning, (4) incentives to innovate, (5) professional development and support for faculty, (6) learner support services, and (7) issues involving quality standards and the impact on student enrollment and retention. The micro-level focused on three areas: (1) instructional design and pedagogical approaches, (2) culturally appropriate materials, interaction, communication, and collaboration among a community of learners, and (3) focus on characteristics of adult learners, socio-economic backgrounds, learning preferences, and dispositions.

The top three research themes in this review by Zawacki-Richter et al. (2009) were interaction and communities of learning ( n  = 122, 17.6%), instructional design ( n  = 121, 17.4%) and learner characteristics ( n  = 113, 16.3%). The lowest number of studies (less than 3%) were found in studies examining the following research themes, management and organization ( n  = 18), research methods in DE and knowledge transfer ( n  = 13), globalization of education and cross-cultural aspects ( n  = 13), innovation and change ( n  = 13), and costs and benefits ( n  = 12).

2.4. Online learning research themes

These three systematic reviews provide a broad understanding of distance education and online learning research themes from 1990 to 2008. However, there is an increase in the number of research studies on online learning in this decade and there is a need to identify recent research themes examined. Based on the previous systematic reviews ( Berge & Mrozowski, 2001 ; Hung, 2012 ; Tallent-Runnels et al., 2006 ; Zawacki-Richter et al., 2009 ), online learning research in this study is grouped into twelve different research themes which include Learner characteristics, Instructor characteristics, Course or program design and development, Course Facilitation, Engagement, Course Assessment, Course Technologies, Access, Culture, Equity, Inclusion, and Ethics, Leadership, Policy and Management, Instructor and Learner Support, and Learner Outcomes. Table 2 below describes each of the research themes and using these themes, a framework is derived in Fig. 1 .

Research themes in online learning.

Online learning research themes framework.

The collection of research themes is presented as a framework in Fig. 1 . The themes are organized by domain or level to underscore the nested relationship that exists. As evidenced by the assortment of themes, research can focus on any domain of delivery or associated context. The “Learner” domain captures characteristics and outcomes related to learners and their interaction within the courses. The “Course and Instructor” domain captures elements about the broader design of the course and facilitation by the instructor, and the “Organizational” domain acknowledges the contextual influences on the course. It is important to note as well that due to the nesting, research themes can cross domains. For example, the broader cultural context may be studied as it pertains to course design and development, and institutional support can include both learner support and instructor support. Likewise, engagement research can involve instructors as well as learners.

In this introduction section, we have reviewed three systematic reviews on online learning research ( Berge & Mrozowski, 2001 ; Tallent-Runnels et al., 2006 ; Zawacki-Richter et al., 2009 ). Based on these reviews and other research, we have derived twelve themes to develop an online learning research framework which is nested in three levels: learner, course and instructor, and organization.

2.5. Purpose of this research

In two out of the three previous reviews, design, learner characteristics and interaction were examined in the highest number of studies. On the other hand, cost-benefit tradeoffs, equity and accessibility, institutional and administrative factors, and globalization and cross-cultural aspects were examined in the least number of studies. One explanation for this may be that it is a function of nesting, noting that studies falling in the Organizational and Course levels may encompass several courses or many more participants within courses. However, while some research themes re-occur, there are also variations in some themes across time, suggesting the importance of research themes rise and fall over time. Thus, a critical examination of the trends in themes is helpful for understanding where research is needed most. Also, since there is no recent study examining online learning research themes in the last decade, this study strives to address that gap by focusing on recent research themes found in the literature, and also reviewing research methods and settings. Notably, one goal is to also compare findings from this decade to the previous review studies. Overall, the purpose of this study is to examine publication trends in online learning research taking place during the last ten years and compare it with the previous themes identified in other review studies. Due to the continued growth of online learning research into new contexts and among new researchers, we also examine the research methods and settings found in the studies of this review.

The following research questions are addressed in this study.

• 1. What percentage of the population of articles published in the journals reviewed from 2009 to 2018 were related to online learning and empirical?
• 2. What is the frequency of online learning research themes in the empirical online learning articles of journals reviewed from 2009 to 2018?
• 3. What is the frequency of research methods and settings that researchers employed in the empirical online learning articles of the journals reviewed from 2009 to 2018?

This five-step systematic review process described in the U.S. Department of Education, Institute of Education Sciences, What Works Clearinghouse Procedures and Standards Handbook, Version 4.0 ( 2017 ) was used in this systematic review: (a) developing the review protocol, (b) identifying relevant literature, (c) screening studies, (d) reviewing articles, and (e) reporting findings.

3.1. Data sources and search strategies

The Education Research Complete database was searched using the keywords below for published articles between the years 2009 and 2018 using both the Title and Keyword function for the following search terms.

“online learning" OR "online teaching" OR "online program" OR "online course" OR “online education”

3.2. Inclusion/exclusion criteria

The initial search of online learning research among journals in the database resulted in more than 3000 possible articles. Therefore, we limited our search to select journals that focus on publishing peer-reviewed online learning and educational research. Our aim was to capture the journals that published the most articles in online learning. However, we also wanted to incorporate the concept of rigor, so we used expert perception to identify 12 peer-reviewed journals that publish high-quality online learning research. Dissertations and conference proceedings were excluded. To be included in this systematic review, each study had to meet the screening criteria as described in Table 3 . A research study was excluded if it did not meet all of the criteria to be included.

Inclusion/Exclusion criteria.

3.3. Process flow selection of articles

Fig. 2 shows the process flow involved in the selection of articles. The search in the database Education Research Complete yielded an initial sample of 3332 articles. Targeting the 12 journals removed 2579 articles. After reviewing the abstracts, we removed 134 articles based on the inclusion/exclusion criteria. The final sample, consisting of 619 articles, was entered into the computer software MAXQDA ( VERBI Software, 2019 ) for coding.

Flowchart of online learning research selection.

3.4. Developing review protocol

A review protocol was designed as a codebook in MAXQDA ( VERBI Software, 2019 ) by the three researchers. The codebook was developed based on findings from the previous review studies and from the initial screening of the articles in this review. The codebook included 12 research themes listed earlier in Table 2 (Learner characteristics, Instructor characteristics, Course or program design and development, Course Facilitation, Engagement, Course Assessment, Course Technologies, Access, Culture, Equity, Inclusion, and Ethics, Leadership, Policy and Management, Instructor and Learner Support, and Learner Outcomes), four research settings (higher education, continuing education, K-12, corporate/military), and three research designs (quantitative, qualitative and mixed methods). Fig. 3 below is a screenshot of MAXQDA used for the coding process.

Codebook from MAXQDA.

3.5. Data coding

Research articles were coded by two researchers in MAXQDA. Two researchers independently coded 10% of the articles and then discussed and updated the coding framework. The second author who was a doctoral student coded the remaining studies. The researchers met bi-weekly to address coding questions that emerged. After the first phase of coding, we found that more than 100 studies fell into each of the categories of Learner Characteristics or Engagement, so we decided to pursue a second phase of coding and reexamine the two themes. Learner Characteristics were classified into the subthemes of Academic, Affective, Motivational, Self-regulation, Cognitive, and Demographic Characteristics. Engagement was classified into the subthemes of Collaborating, Communication, Community, Involvement, Interaction, Participation, and Presence.

3.6. Data analysis

Frequency tables were generated for each of the variables so that outliers could be examined and narrative data could be collapsed into categories. Once cleaned and collapsed into a reasonable number of categories, descriptive statistics were used to describe each of the coded elements. We first present the frequencies of publications related to online learning in the 12 journals. The total number of articles for each journal (collectively, the population) was hand-counted from journal websites, excluding editorials and book reviews. The publication trend of online learning research was also depicted from 2009 to 2018. Then, the descriptive information of the 12 themes, including the subthemes of Learner Characteristics and Engagement were provided. Finally, research themes by research settings and methodology were elaborated.

4.1. Publication trends on online learning

Publication patterns of the 619 articles reviewed from the 12 journals are presented in Table 4 . International Review of Research in Open and Distributed Learning had the highest number of publications in this review. Overall, about 8% of the articles appearing in these twelve journals consisted of online learning publications; however, several journals had concentrations of online learning articles totaling more than 20%.

Empirical online learning research articles by journal, 2009–2018.

Note . Journal's Total Article count excludes reviews and editorials.

The publication trend of online learning research is depicted in Fig. 4 . When disaggregated by year, the total frequency of publications shows an increasing trend. Online learning articles increased throughout the decade and hit a relative maximum in 2014. The greatest number of online learning articles ( n  = 86) occurred most recently, in 2018.

Online learning publication trends by year.

4.2. Online learning research themes that appeared in the selected articles

The publications were categorized into the twelve research themes identified in Fig. 1 . The frequency counts and percentages of the research themes are provided in Table 5 below. A majority of the research is categorized into the Learner domain. The fewest number of articles appears in the Organization domain.

Research themes in the online learning publications from 2009 to 2018.

The specific themes of Engagement ( n  = 179, 28.92%) and Learner Characteristics ( n  = 134, 21.65%) were most often examined in publications. These two themes were further coded to identify sub-themes, which are described in the next two sections. Publications focusing on Instructor Characteristics ( n  = 21, 3.39%) were least common in the dataset.

4.2.1. Research on engagement

The largest number of studies was on engagement in online learning, which in the online learning literature is referred to and examined through different terms. Hence, we explore this category in more detail. In this review, we categorized the articles into seven different sub-themes as examined through different lenses including presence, interaction, community, participation, collaboration, involvement, and communication. We use the term “involvement” as one of the terms since researchers sometimes broadly used the term engagement to describe their work without further description. Table 6 below provides the description, frequency, and percentages of the various studies related to engagement.

Research sub-themes on engagement.

In the sections below, we provide several examples of the different engagement sub-themes that were studied within the larger engagement theme.

Presence. This sub-theme was the most researched in engagement. With the development of the community of inquiry framework most of the studies in this subtheme examined social presence ( Akcaoglu & Lee, 2016 ; Phirangee & Malec, 2017 ; Wei et al., 2012 ), teaching presence ( Orcutt & Dringus, 2017 ; Preisman, 2014 ; Wisneski et al., 2015 ) and cognitive presence ( Archibald, 2010 ; Olesova et al., 2016 ).

Interaction . This was the second most studied theme under engagement. Researchers examined increasing interpersonal interactions ( Cung et al., 2018 ), learner-learner interactions ( Phirangee, 2016 ; Shackelford & Maxwell, 2012 ; Tawfik et al., 2018 ), peer-peer interaction ( Comer et al., 2014 ), learner-instructor interaction ( Kuo et al., 2014 ), learner-content interaction ( Zimmerman, 2012 ), interaction through peer mentoring ( Ruane & Koku, 2014 ), interaction and community building ( Thormann & Fidalgo, 2014 ), and interaction in discussions ( Ruane & Lee, 2016 ; Tibi, 2018 ).

Community. Researchers examined building community in online courses ( Berry, 2017 ), supporting a sense of community ( Jiang, 2017 ), building an online learning community of practice ( Cho, 2016 ), building an academic community ( Glazer & Wanstreet, 2011 ; Nye, 2015 ; Overbaugh & Nickel, 2011 ), and examining connectedness and rapport in an online community ( Bolliger & Inan, 2012 ; Murphy & Rodríguez-Manzanares, 2012 ; Slagter van Tryon & Bishop, 2012 ).

Participation. Researchers examined engagement through participation in a number of studies. Some of the topics include, participation patterns in online discussion ( Marbouti & Wise, 2016 ; Wise et al., 2012 ), participation in MOOCs ( Ahn et al., 2013 ; Saadatmand & Kumpulainen, 2014 ), features that influence students’ online participation ( Rye & Støkken, 2012 ) and active participation.

Collaboration. Researchers examined engagement through collaborative learning. Specific studies focused on cross-cultural collaboration ( Kumi-Yeboah, 2018 ; Yang et al., 2014 ), how virtual teams collaborate ( Verstegen et al., 2018 ), types of collaboration teams ( Wicks et al., 2015 ), tools for collaboration ( Boling et al., 2014 ), and support for collaboration ( Kopp et al., 2012 ).

Involvement. Researchers examined engaging learners through involvement in various learning activities ( Cundell & Sheepy, 2018 ), student engagement through various measures ( Dixson, 2015 ), how instructors included engagement to involve students in learning ( O'Shea et al., 2015 ), different strategies to engage the learner ( Amador & Mederer, 2013 ), and designed emotionally engaging online environments ( Koseoglu & Doering, 2011 ).

Communication. Researchers examined communication in online learning in studies using social network analysis ( Ergün & Usluel, 2016 ), using informal communication tools such as Facebook for class discussion ( Kent, 2013 ), and using various modes of communication ( Cunningham et al., 2010 ; Rowe, 2016 ). Studies have also focused on both asynchronous and synchronous aspects of communication ( Swaggerty & Broemmel, 2017 ; Yamagata-Lynch, 2014 ).

4.2.2. Research on learner characteristics

The second largest theme was learner characteristics. In this review, we explore this further to identify several aspects of learner characteristics. In this review, we categorized the learner characteristics into self-regulation characteristics, motivational characteristics, academic characteristics, affective characteristics, cognitive characteristics, and demographic characteristics. Table 7 provides the number of studies and percentages examining the various learner characteristics.

Research sub-themes on learner characteristics.

Online learning has elements that are different from the traditional face-to-face classroom and so the characteristics of the online learners are also different. Yukselturk and Top (2013) categorized online learner profile into ten aspects: gender, age, work status, self-efficacy, online readiness, self-regulation, participation in discussion list, participation in chat sessions, satisfaction, and achievement. Their categorization shows that there are differences in online learner characteristics in these aspects when compared to learners in other settings. Some of the other aspects such as participation and achievement as discussed by Yukselturk and Top (2013) are discussed in different research themes in this study. The sections below provide examples of the learner characteristics sub-themes that were studied.

Self-regulation. Several researchers have examined self-regulation in online learning. They found that successful online learners are academically motivated ( Artino & Stephens, 2009 ), have academic self-efficacy ( Cho & Shen, 2013 ), have grit and intention to succeed ( Wang & Baker, 2018 ), have time management and elaboration strategies ( Broadbent, 2017 ), set goals and revisit course content ( Kizilcec et al., 2017 ), and persist ( Glazer & Murphy, 2015 ). Researchers found a positive relationship between learner's self-regulation and interaction ( Delen et al., 2014 ) and self-regulation and communication and collaboration ( Barnard et al., 2009 ).

Motivation. Researchers focused on motivation of online learners including different motivation levels of online learners ( Li & Tsai, 2017 ), what motivated online learners ( Chaiprasurt & Esichaikul, 2013 ), differences in motivation of online learners ( Hartnett et al., 2011 ), and motivation when compared to face to face learners ( Paechter & Maier, 2010 ). Harnett et al. (2011) found that online learner motivation was complex, multifaceted, and sensitive to situational conditions.

Academic. Several researchers have focused on academic aspects for online learner characteristics. Readiness for online learning has been examined as an academic factor by several researchers ( Buzdar et al., 2016 ; Dray et al., 2011 ; Wladis & Samuels, 2016 ; Yu, 2018 ) specifically focusing on creating and validating measures to examine online learner readiness including examining students emotional intelligence as a measure of student readiness for online learning. Researchers have also examined other academic factors such as academic standing ( Bradford & Wyatt, 2010 ), course level factors ( Wladis et al., 2014 ) and academic skills in online courses ( Shea & Bidjerano, 2014 ).

Affective. Anderson and Bourke (2013) describe affective characteristics through which learners express feelings or emotions. Several research studies focused on the affective characteristics of online learners. Learner satisfaction for online learning has been examined by several researchers ( Cole et al., 2014 ; Dziuban et al., 2015 ; Kuo et al., 2013 ; Lee, 2014a ) along with examining student emotions towards online assessment ( Kim et al., 2014 ).

Cognitive. Researchers have also examined cognitive aspects of learner characteristics including meta-cognitive skills, cognitive variables, higher-order thinking, cognitive density, and critical thinking ( Chen & Wu, 2012 ; Lee, 2014b ). Lee (2014b) examined the relationship between cognitive presence density and higher-order thinking skills. Chen and Wu (2012) examined the relationship between cognitive and motivational variables in an online system for secondary physical education.

Demographic. Researchers have examined various demographic factors in online learning. Several researchers have examined gender differences in online learning ( Bayeck et al., 2018 ; Lowes et al., 2016 ; Yukselturk & Bulut, 2009 ), ethnicity, age ( Ke & Kwak, 2013 ), and minority status ( Yeboah & Smith, 2016 ) of online learners.

4.2.3. Less frequently studied research themes

While engagement and learner characteristics were studied the most, other themes were less often studied in the literature and are presented here, according to size, with general descriptions of the types of research examined for each.

Evaluation and Quality Assurance. There were 38 studies (6.14%) published in the theme of evaluation and quality assurance. Some of the studies in this theme focused on course quality standards, using quality matters to evaluate quality, using the CIPP model for evaluation, online learning system evaluation, and course and program evaluations.

Course Technologies. There were 35 studies (5.65%) published in the course technologies theme. Some of the studies examined specific technologies such as Edmodo, YouTube, Web 2.0 tools, wikis, Twitter, WebCT, Screencasts, and Web conferencing systems in the online learning context.

Course Facilitation. There were 34 studies (5.49%) published in the course facilitation theme. Some of the studies in this theme examined facilitation strategies and methods, experiences of online facilitators, and online teaching methods.

Institutional Support. There were 33 studies (5.33%) published in the institutional support theme which included support for both the instructor and learner. Some of the studies on instructor support focused on training new online instructors, mentoring programs for faculty, professional development resources for faculty, online adjunct faculty training, and institutional support for online instructors. Studies on learner support focused on learning resources for online students, cognitive and social support for online learners, and help systems for online learner support.

Learner Outcome. There were 32 studies (5.17%) published in the learner outcome theme. Some of the studies that were examined in this theme focused on online learner enrollment, completion, learner dropout, retention, and learner success.

Course Assessment. There were 30 studies (4.85%) published in the course assessment theme. Some of the studies in the course assessment theme examined online exams, peer assessment and peer feedback, proctoring in online exams, and alternative assessments such as eportfolio.

Access, Culture, Equity, Inclusion, and Ethics. There were 29 studies (4.68%) published in the access, culture, equity, inclusion, and ethics theme. Some of the studies in this theme examined online learning across cultures, multi-cultural effectiveness, multi-access, and cultural diversity in online learning.

Leadership, Policy, and Management. There were 27 studies (4.36%) published in the leadership, policy, and management theme. Some of the studies on leadership, policy, and management focused on online learning leaders, stakeholders, strategies for online learning leadership, resource requirements, university policies for online course policies, governance, course ownership, and faculty incentives for online teaching.

Course Design and Development. There were 27 studies (4.36%) published in the course design and development theme. Some of the studies examined in this theme focused on design elements, design issues, design process, design competencies, design considerations, and instructional design in online courses.

Instructor Characteristics. There were 21 studies (3.39%) published in the instructor characteristics theme. Some of the studies in this theme were on motivation and experiences of online instructors, ability to perform online teaching duties, roles of online instructors, and adjunct versus full-time online instructors.

4.3. Research settings and methodology used in the studies

The research methods used in the studies were classified into quantitative, qualitative, and mixed methods ( Harwell, 2012 , pp. 147–163). The research setting was categorized into higher education, continuing education, K-12, and corporate/military. As shown in Table A in the appendix, the vast majority of the publications used higher education as the research setting ( n  = 509, 67.6%). Table B in the appendix shows that approximately half of the studies adopted the quantitative method ( n  = 324, 43.03%), followed by the qualitative method ( n  = 200, 26.56%). Mixed methods account for the smallest portion ( n  = 95, 12.62%).

Table A shows that the patterns of the four research settings were approximately consistent across the 12 themes except for the theme of Leaner Outcome and Institutional Support. Continuing education had a higher relative frequency in Learner Outcome (0.28) and K-12 had a higher relative frequency in Institutional Support (0.33) compared to the frequencies they had in the total themes (0.09 and 0.08 respectively). Table B in the appendix shows that the distribution of the three methods were not consistent across the 12 themes. While quantitative studies and qualitative studies were roughly evenly distributed in Engagement, they had a large discrepancy in Learner Characteristics. There were 100 quantitative studies; however, only 18 qualitative studies published in the theme of Learner Characteristics.

In summary, around 8% of the articles published in the 12 journals focus on online learning. Online learning publications showed a tendency of increase on the whole in the past decade, albeit fluctuated, with the greatest number occurring in 2018. Among the 12 research themes related to online learning, the themes of Engagement and Learner Characteristics were studied the most and the theme of Instructor Characteristics was studied the least. Most studies were conducted in the higher education setting and approximately half of the studies used the quantitative method. Looking at the 12 themes by setting and method, we found that the patterns of the themes by setting or by method were not consistent across the 12 themes.

The quality of our findings was ensured by scientific and thorough searches and coding consistency. The selection of the 12 journals provides evidence of the representativeness and quality of primary studies. In the coding process, any difficulties and questions were resolved by consultations with the research team at bi-weekly meetings, which ensures the intra-rater and interrater reliability of coding. All these approaches guarantee the transparency and replicability of the process and the quality of our results.

5. Discussion

This review enabled us to identify the online learning research themes examined from 2009 to 2018. In the section below, we review the most studied research themes, engagement and learner characteristics along with implications, limitations, and directions for future research.

5.1. Most studied research themes

Three out of the four systematic reviews informing the design of the present study found that online learner characteristics and online engagement were examined in a high number of studies. In this review, about half of the studies reviewed (50.57%) focused on online learner characteristics or online engagement. This shows the continued importance of these two themes. In the Tallent-Runnels et al.’s (2006) study, the learner characteristics theme was identified as least studied for which they state that researchers are beginning to investigate learner characteristics in the early days of online learning.

One of the differences found in this review is that course design and development was examined in the least number of studies in this review compared to two prior systematic reviews ( Berge & Mrozowski, 2001 ; Zawacki-Richter et al., 2009 ). Zawacki-Richter et al. did not use a keyword search but reviewed all the articles in five different distance education journals. Berge and Mrozowski (2001) included a research theme called design issues to include all aspects of instructional systems design in distance education journals. In our study, in addition to course design and development, we also had focused themes on learner outcomes, course facilitation, course assessment and course evaluation. These are all instructional design focused topics and since we had multiple themes focusing on instructional design topics, the course design and development category might have resulted in fewer studies. There is still a need for more studies to focus on online course design and development.

5.2. Least frequently studied research themes

Three out of the four systematic reviews discussed in the opening of this study found management and organization factors to be least studied. In this review, Leadership, Policy, and Management was studied among 4.36% of the studies and Access, Culture, Equity, Inclusion, and Ethics was studied among 4.68% of the studies in the organizational level. The theme on Equity and accessibility was also found to be the least studied theme in the Berge and Mrozowski (2001) study. In addition, instructor characteristics was the least examined research theme among the twelve themes studied in this review. Only 3.39% of the studies were on instructor characteristics. While there were some studies examining instructor motivation and experiences, instructor ability to teach online, online instructor roles, and adjunct versus full-time online instructors, there is still a need to examine topics focused on instructors and online teaching. This theme was not included in the prior reviews as the focus was more on the learner and the course but not on the instructor. While it is helpful to see research evolving on instructor focused topics, there is still a need for more research on the online instructor.

5.3. Comparing research themes from current study to previous studies

The research themes from this review were compared with research themes from previous systematic reviews, which targeted prior decades. Table 8 shows the comparison.

Comparison of most and least studied online learning research themes from current to previous reviews.

L = Learner, C=Course O=Organization.

5.4. Need for more studies on organizational level themes of online learning

In this review there is a greater concentration of studies focused on Learner domain topics, and reduced attention to broader more encompassing research themes that fall into the Course and Organization domains. There is a need for organizational level topics such as Access, Culture, Equity, Inclusion and Ethics, and Leadership, Policy and Management to be researched on within the context of online learning. Examination of access, culture, equity, inclusion and ethics is very important to support diverse online learners, particularly with the rapid expansion of online learning across all educational levels. This was also least studied based on Berge and Mrozowski (2001) systematic review.

The topics on leadership, policy and management were least studied both in this review and also in the Tallent-Runnels et al. (2006) and Zawacki-Richter et al. (2009) study. Tallent-Runnels categorized institutional and administrative aspects into institutional policies, institutional support, and enrollment effects. While we included support as a separate category, in this study leadership, policy and management were combined. There is still a need for research on leadership of those who manage online learning, policies for online education, and managing online programs. In the Zawacki-Richter et al. (2009) study, only a few studies examined management and organization focused topics. They also found management and organization to be strongly correlated with costs and benefits. In our study, costs and benefits were collectively included as an aspect of management and organization and not as a theme by itself. These studies will provide research-based evidence for online education administrators.

6. Limitations

As with any systematic review, there are limitations to the scope of the review. The search is limited to twelve journals in the field that typically include research on online learning. These manuscripts were identified by searching the Education Research Complete database which focuses on education students, professionals, and policymakers. Other discipline-specific journals as well as dissertations and proceedings were not included due to the volume of articles. Also, the search was performed using five search terms “online learning" OR "online teaching" OR "online program" OR "online course" OR “online education” in title and keyword. If authors did not include these terms, their respective work may have been excluded from this review even if it focused on online learning. While these terms are commonly used in North America, it may not be commonly used in other parts of the world. Additional studies may exist outside this scope.

The search strategy also affected how we presented results and introduced limitations regarding generalization. We identified that only 8% of the articles published in these journals were related to online learning; however, given the use of search terms to identify articles within select journals it was not feasible to identify the total number of research-based articles in the population. Furthermore, our review focused on the topics and general methods of research and did not systematically consider the quality of the published research. Lastly, some journals may have preferences for publishing studies on a particular topic or that use a particular method (e.g., quantitative methods), which introduces possible selection and publication biases which may skew the interpretation of results due to over/under representation. Future studies are recommended to include more journals to minimize the selection bias and obtain a more representative sample.

Certain limitations can be attributed to the coding process. Overall, the coding process for this review worked well for most articles, as each tended to have an individual or dominant focus as described in the abstracts, though several did mention other categories which likely were simultaneously considered to a lesser degree. However, in some cases, a dominant theme was not as apparent and an effort to create mutually exclusive groups for clearer interpretation the coders were occasionally forced to choose between two categories. To facilitate this coding, the full-texts were used to identify a study focus through a consensus seeking discussion among all authors. Likewise, some studies focused on topics that we have associated with a particular domain, but the design of the study may have promoted an aggregated examination or integrated factors from multiple domains (e.g., engagement). Due to our reliance on author descriptions, the impact of construct validity is likely a concern that requires additional exploration. Our final grouping of codes may not have aligned with the original author's description in the abstract. Additionally, coding of broader constructs which disproportionately occur in the Learner domain, such as learner outcomes, learner characteristics, and engagement, likely introduced bias towards these codes when considering studies that involved multiple domains. Additional refinement to explore the intersection of domains within studies is needed.

7. Implications and future research

One of the strengths of this review is the research categories we have identified. We hope these categories will support future researchers and identify areas and levels of need for future research. Overall, there is some agreement on research themes on online learning research among previous reviews and this one, at the same time there are some contradicting findings. We hope the most-researched themes and least-researched themes provide authors a direction on the importance of research and areas of need to focus on.

The leading themes found in this review is online engagement research. However, presentation of this research was inconsistent, and often lacked specificity. This is not unique to online environments, but the nuances of defining engagement in an online environment are unique and therefore need further investigation and clarification. This review points to seven distinct classifications of online engagement. Further research on engagement should indicate which type of engagement is sought. This level of specificity is necessary to establish instruments for measuring engagement and ultimately testing frameworks for classifying engagement and promoting it in online environments. Also, it might be of importance to examine the relationship between these seven sub-themes of engagement.

Additionally, this review highlights growing attention to learner characteristics, which constitutes a shift in focus away from instructional characteristics and course design. Although this is consistent with the focus on engagement, the role of the instructor, and course design with respect to these outcomes remains important. Results of the learner characteristics and engagement research paired with course design will have important ramifications for the use of teaching and learning professionals who support instruction. Additionally, the review also points to a concentration of research in the area of higher education. With an immediate and growing emphasis on online learning in K-12 and corporate settings, there is a critical need for further investigation in these settings.

Lastly, because the present review did not focus on the overall effect of interventions, opportunities exist for dedicated meta-analyses. Particular attention to research on engagement and learner characteristics as well as how these vary by study design and outcomes would be logical additions to the research literature.

8. Conclusion

This systematic review builds upon three previous reviews which tackled the topic of online learning between 1990 and 2010 by extending the timeframe to consider the most recent set of published research. Covering the most recent decade, our review of 619 articles from 12 leading online learning journal points to a more concentrated focus on the learner domain including engagement and learner characteristics, with more limited attention to topics pertaining to the classroom or organizational level. The review highlights an opportunity for the field to clarify terminology concerning online learning research, particularly in the areas of learner outcomes where there is a tendency to classify research more generally (e.g., engagement). Using this sample of published literature, we provide a possible taxonomy for categorizing this research using subcategories. The field could benefit from a broader conversation about how these categories can shape a comprehensive framework for online learning research. Such efforts will enable the field to effectively prioritize research aims over time and synthesize effects.

Credit author statement

Florence Martin: Conceptualization; Writing - original draft, Writing - review & editing Preparation, Supervision, Project administration. Ting Sun: Methodology, Formal analysis, Writing - original draft, Writing - review & editing. Carl Westine: Methodology, Formal analysis, Writing - original draft, Writing - review & editing, Supervision

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

1 Includes articles that are cited in this manuscript and also included in the systematic review. The entire list of 619 articles used in the systematic review can be obtained by emailing the authors.*

Appendix B Supplementary data to this article can be found online at https://doi.org/10.1016/j.compedu.2020.104009 .

Appendix A. 

Research Themes by the Settings in the Online Learning Publications

Research Themes by the Methodology in the Online Learning Publications

Appendix B. Supplementary data

The following are the Supplementary data to this article:

References 1

• Ahn J., Butler B.S., Alam A., Webster S.A. Learner participation and engagement in open online courses: Insights from the Peer 2 Peer University. MERLOT Journal of Online Learning and Teaching. 2013; 9 (2):160–171. * [ Google Scholar ]
• Akcaoglu M., Lee E. Increasing social presence in online learning through small group discussions. International Review of Research in Open and Distance Learning. 2016; 17 (3) * [ Google Scholar ]
• Allen I.E., Seaman J. Babson survey research group; 2017. Digital compass learning: Distance education enrollment Report 2017. [ Google Scholar ]
• Amador J.A., Mederer H. Migrating successful student engagement strategies online: Opportunities and challenges using jigsaw groups and problem-based learning. Journal of Online Learning and Teaching. 2013; 9 (1):89. * [ Google Scholar ]
• Anderson L.W., Bourke S.F. Routledge; 2013. Assessing affective characteristics in the schools. [ Google Scholar ]
• Archibald D. Fostering the development of cognitive presence: Initial findings using the community of inquiry survey instrument. The Internet and Higher Education. 2010; 13 (1–2):73–74. * [ Google Scholar ]
• Artino A.R., Jr., Stephens J.M. Academic motivation and self-regulation: A comparative analysis of undergraduate and graduate students learning online. The Internet and Higher Education. 2009; 12 (3–4):146–151. [ Google Scholar ]
• Barnard L., Lan W.Y., To Y.M., Paton V.O., Lai S.L. Measuring self-regulation in online and blended learning environments. Internet and Higher Education. 2009; 12 (1):1–6. * [ Google Scholar ]
• Bayeck R.Y., Hristova A., Jablokow K.W., Bonafini F. Exploring the relevance of single‐gender group formation: What we learn from a massive open online course (MOOC) British Journal of Educational Technology. 2018; 49 (1):88–100. * [ Google Scholar ]
• Berge Z., Mrozowski S. Review of research in distance education, 1990 to 1999. American Journal of Distance Education. 2001; 15 (3):5–19. doi: 10.1080/08923640109527090. [ CrossRef ] [ Google Scholar ]
• Berry S. Building community in online doctoral classrooms: Instructor practices that support community. Online Learning. 2017; 21 (2):n2. * [ Google Scholar ]
• Boling E.C., Holan E., Horbatt B., Hough M., Jean-Louis J., Khurana C., Spiezio C. Using online tools for communication and collaboration: Understanding educators' experiences in an online course. The Internet and Higher Education. 2014; 23 :48–55. * [ Google Scholar ]
• Bolliger D.U., Inan F.A. Development and validation of the online student connectedness survey (OSCS) International Review of Research in Open and Distance Learning. 2012; 13 (3):41–65. * [ Google Scholar ]
• Bradford G., Wyatt S. Online learning and student satisfaction: Academic standing, ethnicity and their influence on facilitated learning, engagement, and information fluency. The Internet and Higher Education. 2010; 13 (3):108–114. * [ Google Scholar ]
• Broadbent J. Comparing online and blended learner's self-regulated learning strategies and academic performance. The Internet and Higher Education. 2017; 33 :24–32. [ Google Scholar ]
• Buzdar M., Ali A., Tariq R. Emotional intelligence as a determinant of readiness for online learning. International Review of Research in Open and Distance Learning. 2016; 17 (1) * [ Google Scholar ]
• Capdeferro N., Romero M., Barberà E. Polychronicity: Review of the literature and a new configuration for the study of this hidden dimension of online learning. Distance Education. 2014; 35 (3):294–310. [ Google Scholar ]
• Chaiprasurt C., Esichaikul V. Enhancing motivation in online courses with mobile communication tool support: A comparative study. International Review of Research in Open and Distance Learning. 2013; 14 (3):377–401. [ Google Scholar ]
• Chen C.H., Wu I.C. The interplay between cognitive and motivational variables in a supportive online learning system for secondary physical education. Computers & Education. 2012; 58 (1):542–550. * [ Google Scholar ]
• Cho H. Under co-construction: An online community of practice for bilingual pre-service teachers. Computers & Education. 2016; 92 :76–89. * [ Google Scholar ]
• Cho M.H., Shen D. Self-regulation in online learning. Distance Education. 2013; 34 (3):290–301. [ Google Scholar ]
• Cole M.T., Shelley D.J., Swartz L.B. Online instruction, e-learning, and student satisfaction: A three-year study. International Review of Research in Open and Distance Learning. 2014; 15 (6) * [ Google Scholar ]
• Comer D.K., Clark C.R., Canelas D.A. Writing to learn and learning to write across the disciplines: Peer-to-peer writing in introductory-level MOOCs. International Review of Research in Open and Distance Learning. 2014; 15 (5):26–82. * [ Google Scholar ]
• Cundell A., Sheepy E. Student perceptions of the most effective and engaging online learning activities in a blended graduate seminar. Online Learning. 2018; 22 (3):87–102. * [ Google Scholar ]
• Cung B., Xu D., Eichhorn S. Increasing interpersonal interactions in an online course: Does increased instructor email activity and voluntary meeting time in a physical classroom facilitate student learning? Online Learning. 2018; 22 (3):193–215. [ Google Scholar ]
• Cunningham U.M., Fägersten K.B., Holmsten E. Can you hear me, Hanoi?" Compensatory mechanisms employed in synchronous net-based English language learning. International Review of Research in Open and Distance Learning. 2010; 11 (1):161–177. [ Google Scholar ]
• Davis D., Chen G., Hauff C., Houben G.J. Activating learning at scale: A review of innovations in online learning strategies. Computers & Education. 2018; 125 :327–344. [ Google Scholar ]
• Delen E., Liew J., Willson V. Effects of interactivity and instructional scaffolding on learning: Self-regulation in online video-based environments. Computers & Education. 2014; 78 :312–320. [ Google Scholar ]
• Dixson M.D. Measuring student engagement in the online course: The Online Student Engagement scale (OSE) Online Learning. 2015; 19 (4):n4. * [ Google Scholar ]
• Dray B.J., Lowenthal P.R., Miszkiewicz M.J., Ruiz‐Primo M.A., Marczynski K. Developing an instrument to assess student readiness for online learning: A validation study. Distance Education. 2011; 32 (1):29–47. * [ Google Scholar ]
• Dziuban C., Moskal P., Thompson J., Kramer L., DeCantis G., Hermsdorfer A. Student satisfaction with online learning: Is it a psychological contract? Online Learning. 2015; 19 (2):n2. * [ Google Scholar ]
• Ergün E., Usluel Y.K. An analysis of density and degree-centrality according to the social networking structure formed in an online learning environment. Journal of Educational Technology & Society. 2016; 19 (4):34–46. * [ Google Scholar ]
• Esfijani A. Measuring quality in online education: A meta-synthesis. American Journal of Distance Education. 2018; 32 (1):57–73. [ Google Scholar ]
• Glazer H.R., Murphy J.A. Optimizing success: A model for persistence in online education. American Journal of Distance Education. 2015; 29 (2):135–144. [ Google Scholar ]
• Glazer H.R., Wanstreet C.E. Connection to the academic community: Perceptions of students in online education. Quarterly Review of Distance Education. 2011; 12 (1):55. * [ Google Scholar ]
• Hartnett M., George A.S., Dron J. Examining motivation in online distance learning environments: Complex, multifaceted and situation-dependent. International Review of Research in Open and Distance Learning. 2011; 12 (6):20–38. [ Google Scholar ]
• Harwell M.R. 2012. Research design in qualitative/quantitative/mixed methods. Section III. Opportunities and challenges in designing and conducting inquiry. [ Google Scholar ]
• Hung J.L. Trends of e‐learning research from 2000 to 2008: Use of text mining and bibliometrics. British Journal of Educational Technology. 2012; 43 (1):5–16. [ Google Scholar ]
• Jiang W. Interdependence of roles, role rotation, and sense of community in an online course. Distance Education. 2017; 38 (1):84–105. [ Google Scholar ]
• Ke F., Kwak D. Online learning across ethnicity and age: A study on learning interaction participation, perception, and learning satisfaction. Computers & Education. 2013; 61 :43–51. [ Google Scholar ]
• Kent M. Changing the conversation: Facebook as a venue for online class discussion in higher education. MERLOT Journal of Online Learning and Teaching. 2013; 9 (4):546–565. * [ Google Scholar ]
• Kim C., Park S.W., Cozart J. Affective and motivational factors of learning in online mathematics courses. British Journal of Educational Technology. 2014; 45 (1):171–185. [ Google Scholar ]
• Kizilcec R.F., Pérez-Sanagustín M., Maldonado J.J. Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses. Computers & Education. 2017; 104 :18–33. [ Google Scholar ]
• Kopp B., Matteucci M.C., Tomasetto C. E-tutorial support for collaborative online learning: An explorative study on experienced and inexperienced e-tutors. Computers & Education. 2012; 58 (1):12–20. [ Google Scholar ]
• Koseoglu S., Doering A. Understanding complex ecologies: An investigation of student experiences in adventure learning programs. Distance Education. 2011; 32 (3):339–355. * [ Google Scholar ]
• Kumi-Yeboah A. Designing a cross-cultural collaborative online learning framework for online instructors. Online Learning. 2018; 22 (4):181–201. * [ Google Scholar ]
• Kuo Y.C., Walker A.E., Belland B.R., Schroder K.E. A predictive study of student satisfaction in online education programs. International Review of Research in Open and Distance Learning. 2013; 14 (1):16–39. * [ Google Scholar ]
• Kuo Y.C., Walker A.E., Schroder K.E., Belland B.R. Interaction, Internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. Internet and Higher Education. 2014; 20 :35–50. * [ Google Scholar ]
• Lee J. An exploratory study of effective online learning: Assessing satisfaction levels of graduate students of mathematics education associated with human and design factors of an online course. International Review of Research in Open and Distance Learning. 2014; 15 (1) [ Google Scholar ]
• Lee S.M. The relationships between higher order thinking skills, cognitive density, and social presence in online learning. The Internet and Higher Education. 2014; 21 :41–52. * [ Google Scholar ]
• Lee K. Rethinking the accessibility of online higher education: A historical review. The Internet and Higher Education. 2017; 33 :15–23. [ Google Scholar ]
• Lee Y., Choi J. A review of online course dropout research: Implications for practice and future research. Educational Technology Research & Development. 2011; 59 (5):593–618. [ Google Scholar ]
• Li L.Y., Tsai C.C. Accessing online learning material: Quantitative behavior patterns and their effects on motivation and learning performance. Computers & Education. 2017; 114 :286–297. [ Google Scholar ]
• Liyanagunawardena T., Adams A., Williams S. MOOCs: A systematic study of the published literature 2008-2012. International Review of Research in Open and Distance Learning. 2013; 14 (3):202–227. [ Google Scholar ]
• Lowes S., Lin P., Kinghorn B.R. Gender differences in online high school courses. Online Learning. 2016; 20 (4):100–117. [ Google Scholar ]
• Marbouti F., Wise A.F. Starburst: A new graphical interface to support purposeful attention to others' posts in online discussions. Educational Technology Research & Development. 2016; 64 (1):87–113. * [ Google Scholar ]
• Martin F., Ahlgrim-Delzell L., Budhrani K. Systematic review of two decades (1995 to 2014) of research on synchronous online learning. American Journal of Distance Education. 2017; 31 (1):3–19. [ Google Scholar ]
• Moore-Adams B.L., Jones W.M., Cohen J. Learning to teach online: A systematic review of the literature on K-12 teacher preparation for teaching online. Distance Education. 2016; 37 (3):333–348. [ Google Scholar ]
• Murphy E., Rodríguez-Manzanares M.A. Rapport in distance education. International Review of Research in Open and Distance Learning. 2012; 13 (1):167–190. * [ Google Scholar ]
• Nye A. Building an online academic learning community among undergraduate students. Distance Education. 2015; 36 (1):115–128. * [ Google Scholar ]
• Olesova L., Slavin M., Lim J. Exploring the effect of scripted roles on cognitive presence in asynchronous online discussions. Online Learning. 2016; 20 (4):34–53. * [ Google Scholar ]
• Orcutt J.M., Dringus L.P. Beyond being there: Practices that establish presence, engage students and influence intellectual curiosity in a structured online learning environment. Online Learning. 2017; 21 (3):15–35. * [ Google Scholar ]
• Overbaugh R.C., Nickel C.E. A comparison of student satisfaction and value of academic community between blended and online sections of a university-level educational foundations course. The Internet and Higher Education. 2011; 14 (3):164–174. * [ Google Scholar ]
• O'Shea S., Stone C., Delahunty J. “I ‘feel’like I am at university even though I am online.” Exploring how students narrate their engagement with higher education institutions in an online learning environment. Distance Education. 2015; 36 (1):41–58. * [ Google Scholar ]
• Paechter M., Maier B. Online or face-to-face? Students' experiences and preferences in e-learning. Internet and Higher Education. 2010; 13 (4):292–297. [ Google Scholar ]
• Phirangee K. Students' perceptions of learner-learner interactions that weaken a sense of community in an online learning environment. Online Learning. 2016; 20 (4):13–33. * [ Google Scholar ]
• Phirangee K., Malec A. Othering in online learning: An examination of social presence, identity, and sense of community. Distance Education. 2017; 38 (2):160–172. * [ Google Scholar ]
• Preisman K.A. Teaching presence in online education: From the instructor's point of view. Online Learning. 2014; 18 (3):n3. * [ Google Scholar ]
• Rowe M. Developing graduate attributes in an open online course. British Journal of Educational Technology. 2016; 47 (5):873–882. * [ Google Scholar ]
• Ruane R., Koku E.F. Social network analysis of undergraduate education student interaction in online peer mentoring settings. Journal of Online Learning and Teaching. 2014; 10 (4):577–589. * [ Google Scholar ]
• Ruane R., Lee V.J. Analysis of discussion board interaction in an online peer mentoring site. Online Learning. 2016; 20 (4):79–99. * [ Google Scholar ]
• Rye S.A., Støkken A.M. The implications of the local context in global virtual education. International Review of Research in Open and Distance Learning. 2012; 13 (1):191–206. * [ Google Scholar ]
• Saadatmand M., Kumpulainen K. Participants' perceptions of learning and networking in connectivist MOOCs. Journal of Online Learning and Teaching. 2014; 10 (1):16. * [ Google Scholar ]
• Shackelford J.L., Maxwell M. Sense of community in graduate online education: Contribution of learner to learner interaction. International Review of Research in Open and Distance Learning. 2012; 13 (4):228–249. * [ Google Scholar ]
• Shea P., Bidjerano T. Does online learning impede degree completion? A national study of community college students. Computers & Education. 2014; 75 :103–111. * [ Google Scholar ]
• Sherry L. Issues in distance learning. International Journal of Educational Telecommunications. 1996; 1 (4):337–365. [ Google Scholar ]
• Slagter van Tryon P.J., Bishop M.J. Evaluating social connectedness online: The design and development of the social perceptions in learning contexts instrument. Distance Education. 2012; 33 (3):347–364. * [ Google Scholar ]
• Swaggerty E.A., Broemmel A.D. Authenticity, relevance, and connectedness: Graduate students' learning preferences and experiences in an online reading education course. The Internet and Higher Education. 2017; 32 :80–86. * [ Google Scholar ]
• Tallent-Runnels M.K., Thomas J.A., Lan W.Y., Cooper S., Ahern T.C., Shaw S.M., Liu X. Teaching courses online: A review of the research. Review of Educational Research. 2006; 76 (1):93–135. doi: 10.3102/00346543076001093. [ CrossRef ] [ Google Scholar ]
• Tawfik A.A., Giabbanelli P.J., Hogan M., Msilu F., Gill A., York C.S. Effects of success v failure cases on learner-learner interaction. Computers & Education. 2018; 118 :120–132. [ Google Scholar ]
• Thomas J. Exploring the use of asynchronous online discussion in health care education: A literature review. Computers & Education. 2013; 69 :199–215. [ Google Scholar ]
• Thormann J., Fidalgo P. Guidelines for online course moderation and community building from a student's perspective. Journal of Online Learning and Teaching. 2014; 10 (3):374–388. * [ Google Scholar ]
• Tibi M.H. Computer science students' attitudes towards the use of structured and unstructured discussion forums in fully online courses. Online Learning. 2018; 22 (1):93–106. * [ Google Scholar ]
• Tsai C.W., Chiang Y.C. Research trends in problem‐based learning (pbl) research in e‐learning and online education environments: A review of publications in SSCI‐indexed journals from 2004 to 2012. British Journal of Educational Technology. 2013; 44 (6):E185–E190. [ Google Scholar ]
• Tsai C.W., Fan Y.T. Research trends in game‐based learning research in online learning environments: A review of studies published in SSCI‐indexed journals from 2003 to 2012. British Journal of Educational Technology. 2013; 44 (5):E115–E119. [ Google Scholar ]
• Tsai C.W., Shen P.D., Chiang Y.C. Research trends in meaningful learning research on e‐learning and online education environments: A review of studies published in SSCI‐indexed journals from 2003 to 2012. British Journal of Educational Technology. 2013; 44 (6):E179–E184. [ Google Scholar ]
• Tsai C.W., Shen P.D., Fan Y.T. Research trends in self‐regulated learning research in online learning environments: A review of studies published in selected journals from 2003 to 2012. British Journal of Educational Technology. 2013; 44 (5):E107–E110. [ Google Scholar ]
• U.S. Department of Education, Institute of Education Sciences . InstituteofEducationSciences; Washington,DC: 2017. What Works Clearinghouse procedures and standards handbook, version3.0. https://ies.ed.gov/ncee/wwc/Docs/referenceresources/wwc_procedures_v3_0_standards_handbook.pdf Retrievedfrom. [ Google Scholar ]
• Veletsianos G., Shepherdson P. A systematic analysis and synthesis of the empirical MOOC literature published in 2013–2015. International Review of Research in Open and Distance Learning. 2016; 17 (2) [ Google Scholar ]
• VERBI Software . 2019. MAXQDA 2020 online manual. Retrieved from maxqda. Com/help-max20/welcome [ Google Scholar ]
• Verstegen D., Dailey-Hebert A., Fonteijn H., Clarebout G., Spruijt A. How do virtual teams collaborate in online learning tasks in a MOOC? International Review of Research in Open and Distance Learning. 2018; 19 (4) * [ Google Scholar ]
• Wang Y., Baker R. Grit and intention: Why do learners complete MOOCs? International Review of Research in Open and Distance Learning. 2018; 19 (3) * [ Google Scholar ]
• Wei C.W., Chen N.S., Kinshuk A model for social presence in online classrooms. Educational Technology Research & Development. 2012; 60 (3):529–545. * [ Google Scholar ]
• Wicks D., Craft B.B., Lee D., Lumpe A., Henrikson R., Baliram N., Wicks K. An evaluation of low versus high collaboration in online learning. Online Learning. 2015; 19 (4):n4. * [ Google Scholar ]
• Wise A.F., Perera N., Hsiao Y.T., Speer J., Marbouti F. Microanalytic case studies of individual participation patterns in an asynchronous online discussion in an undergraduate blended course. The Internet and Higher Education. 2012; 15 (2):108–117. * [ Google Scholar ]
• Wisneski J.E., Ozogul G., Bichelmeyer B.A. Does teaching presence transfer between MBA teaching environments? A comparative investigation of instructional design practices associated with teaching presence. The Internet and Higher Education. 2015; 25 :18–27. * [ Google Scholar ]
• Wladis C., Hachey A.C., Conway K. An investigation of course-level factors as predictors of online STEM course outcomes. Computers & Education. 2014; 77 :145–150. * [ Google Scholar ]
• Wladis C., Samuels J. Do online readiness surveys do what they claim? Validity, reliability, and subsequent student enrollment decisions. Computers & Education. 2016; 98 :39–56. [ Google Scholar ]
• Yamagata-Lynch L.C. Blending online asynchronous and synchronous learning. International Review of Research in Open and Distance Learning. 2014; 15 (2) * [ Google Scholar ]
• Yang J., Kinshuk, Yu H., Chen S.J., Huang R. Strategies for smooth and effective cross-cultural online collaborative learning. Journal of Educational Technology & Society. 2014; 17 (3):208–221. * [ Google Scholar ]
• Yeboah A.K., Smith P. Relationships between minority students online learning experiences and academic performance. Online Learning. 2016; 20 (4):n4. * [ Google Scholar ]
• Yu T. Examining construct validity of the student online learning readiness (SOLR) instrument using confirmatory factor analysis. Online Learning. 2018; 22 (4):277–288. * [ Google Scholar ]
• Yukselturk E., Bulut S. Gender differences in self-regulated online learning environment. Educational Technology & Society. 2009; 12 (3):12–22. [ Google Scholar ]
• Yukselturk E., Top E. Exploring the link among entry characteristics, participation behaviors and course outcomes of online learners: An examination of learner profile using cluster analysis. British Journal of Educational Technology. 2013; 44 (5):716–728. [ Google Scholar ]
• Zawacki-Richter O., Backer E., Vogt S. Review of distance education research (2000 to 2008): Analysis of research areas, methods, and authorship patterns. International Review of Research in Open and Distance Learning. 2009; 10 (6):30. doi: 10.19173/irrodl.v10i6.741. [ CrossRef ] [ Google Scholar ]
• Zhu M., Sari A., Lee M.M. A systematic review of research methods and topics of the empirical MOOC literature (2014–2016) The Internet and Higher Education. 2018; 37 :31–39. [ Google Scholar ]
• Zimmerman T.D. Exploring learner to content interaction as a success factor in online courses. International Review of Research in Open and Distance Learning. 2012; 13 (4):152–165. [ Google Scholar ]

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Data mining of online teaching evaluation based on deep learning, 1. introduction.

• Detailed introduction of dictionary-based sentiment analysis methods, the BERT-LSTM model, and association rule mining.
• Calculation of sentiment scores for unannotated text data using sentiment lexicon methods, determining sentiment tendencies through calculated scores combined with manual judgment.
• Development of a teaching evaluation sentiment analysis model using BERT-LSTM for predicting and analyzing sentiment trends.
• Application of the Apriori algorithm to mine association rules from objective teaching evaluation data, followed by analysis based on the mining results.

2. Related Work

2.1. sentiment analysis, 2.1.1. lexicon-based methods, 2.1.2. machine learning methods, 2.1.3. deep learning methods, 2.2. association rule mining, 3.1. sentiment analysis based on sentiment lexicon, 3.1.1. text preprocessing, 3.1.2. calculating sentiment scores using a sentiment lexicon, 3.2. sentiment analysis based on bert-lstm model, 3.2.1. bert, 3.2.2. lstm, 3.2.3. bert-lstm, 3.3. association rule mining based on the apriori algorithm, 3.3.1. basic concept of association rules, 3.3.2. apriori algorithm, 4. subjective evaluation of teaching analysis, 4.1. experimental data preparation, 4.1.1. preprocessing of subjective evaluation data, 4.1.2. addressing category imbalance, 4.2. performance metrics, 4.3. experimental environment and parameter settings, 4.4. results, 5. objective evaluation of teaching analysis, 5.1. objective evaluation data preprocessing, 5.1.1. data cleaning, 5.1.2. data conversion, 5.2. association rule mining for objective assessment data, 5.2.1. association rule mining results, 5.2.2. association rule analysis, 6. conclusions and future work, author contributions, data availability statement, conflicts of interest.

• Xu, Q.; Li, J.; Wang, Y.; Zhang, L. Student Achievement Analysis and Visualization Based on Apriori algorithm. J. Tonghua Norm. Coll. 2023 , 44 , 81–87. [ Google Scholar ]
• Tang, S. Design and Implementation of Data Mining Based Evaluation System for Colleges and Universities. Master’s Thesis, University of Electronic Science and Technology of China, Chengdu, China, 2010. [ Google Scholar ]
• Liu, Y.; Zhao, Y. Sentiment analysis of Chinese short text based on teaching evaluation. Mod. Electron. Tech. 2019 , 42 , 30–33+37. [ Google Scholar ]
• Wang, Y.; Zhu, J.; Wang, Z.; Bai, F.; Gong, J. Review of applications of natural language processing in text sentiment analysis. J. Comput. Appl. 2022 , 42 , 1011–1020. [ Google Scholar ]
• Baragash, R.; Aldowah, H. Sentiment analysis in higher education: A systematic mapping review. J. Phys. Conf. Ser. 2021 , 1860 , 012002. [ Google Scholar ] [ CrossRef ]
• Mao, C.; Zou, S.; Yin, J. Educational Evaluation Based on Apriori-Gen Algorithm. Eurasia J. Math. Sci. Technol. Educ. 2017 , 13 , 6555–6564. [ Google Scholar ] [ CrossRef ]
• Liu, D.; Zhang, L. Construction of Higher Education Management and Student Achievement Evaluation Mechanism Based on Apriori Algorithm. Mob. Inf. Syst. 2022 , 2022 , 5375825. [ Google Scholar ] [ CrossRef ]
• Lei, P.; Qin, B.; Wang, L.; Wu, Y.; Liang, S.; Chen, Y. PRBDN: A Pretraining-based Emotion Classification Model for Weibo Comment. J. Chin. Inf. Process. 2022 , 36 , 101–108. [ Google Scholar ]
• Öhman, E. The Validity of Lexicon-based Emotion Analysis in Interdisciplinary Research. In Proceedings of the Workshop on Natural Language Processing for Digital Humanities; NLP Association of India (NLPAI), NIT Silchar: Silchar, India, 2021; pp. 7–12. [ Google Scholar ]
• Wankhade, M.; Rao, A.C.S.; Kulkarni, C. A survey on sentiment analysis methods, applications, and challenges. Artif. Intell. Rev. 2022 , 55 , 5731–5780. [ Google Scholar ] [ CrossRef ]
• Aung, K.Z.; Myo, N.N. Sentiment analysis of students’ comment using lexicon based approach. In Proceedings of the 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS), Wuhan, China, 24–26 May 2017; pp. 149–154. [ Google Scholar ]
• Cai, R.; Qin, B.; Chen, Y.; Zhang, L.; Yang, R.; Chen, S.; Wang, W. Sentiment Analysis About Investors and Consumers in Energy Market Based on BERT-BiLSTM. IEEE Access 2020 , 8 , 171408–171415. [ Google Scholar ] [ CrossRef ]
• Ortigosa, A.; Martín, J.M.; Carro, R.M. Sentiment analysis in Facebook and its application to e-learning. Comput. Human Behav. 2014 , 31 , 527–541. [ Google Scholar ] [ CrossRef ]
• Dsouza, D.D.; Deepika, D.P.N.; Machado, E.J.; Adesh, N.D. Sentimental analysis of student feedback using machine learning techniques. Int. J. Recent Technol. Eng. 2019 , 8 , 986–991. [ Google Scholar ]
• Kandhro, I.A.; Wasi, S.; Kumar, K.; Rind, M.; Ameen, M. Sentiment analysis of students’ comment using long-short term model. Indian J. Sci. Technol. 2019 , 12 , 1–16. [ Google Scholar ] [ CrossRef ]
• Zhang, J.; Chen, F.; Zhang, P. The role and implementation of students’ sentiment analysis in curriculum teaching evaluation. Comput. Knowl. Technol. 2019 , 25 , 184–188. [ Google Scholar ]
• Cabada, R.Z.; Estrada, M.L.B.; Bustillos, R.O. Mining of educational opinions with deep learning. J. Univ. Comput. Sci. 2018 , 24 , 1604–1626. [ Google Scholar ]
• Zhang, Y. The Application Research of Data Mining Technique in Teaching Evaluation. Master’s Thesis, Jinan University, Guangzhou, China, 2012. [ Google Scholar ]
• Li, F. Research on Mining Association Rules of Online Teaching Evaluation Data and its Application in Teaching Quality Improvement. Master’s Thesis, Beijing University of Post and Telecommunications, Beijing, China, 2021. [ Google Scholar ]
• Yan, X.; Zhang, K. Application of sentiment analysis technology in postgraduate teaching evaluation text. Comput. Era 2019 , 51–54+58. [ Google Scholar ]
• Pu, Q.; Huang, F.; Wang, H. Research on sentiment analysis based on BERT-LSTM Model. J. China Acad. Electron. Inf. Technol. 2023 , 18 , 912–920. [ Google Scholar ]
• Devlin, J.; Chang, M.W.; Lee, K.; Toutanova, K. BERT: Pre-training of Deep Bidirectional Transformers for Lauguage Understanding. arXiv 2018 , arXiv:1810.04805. [ Google Scholar ]
• Hochreiter, S.; Schmidhuber, J. Long short-term memory. Neural Comput. 1997 , 9 , 1735–1780. [ Google Scholar ] [ CrossRef ]
• Sang, Q.; Wang, S. Sentiment analysis of student evaluation based on text mining technology. China Comput. Commun. 2022 , 34 , 38–41. [ Google Scholar ]
• Ge, Y.; Liu, W.; Gu, Y. Stock price prediction method by fusing sentiment analysis and GAN-TrellisNet. Comput. Eng. Appl. 2024 , 60 , 314. [ Google Scholar ]
• Tang, S. Python Machine Learning Foundation ; Tinghua University Press: Beijing, China, 2022; ISBN 978-730-261-128-8. [ Google Scholar ]
• Jing, G.; Qin, H.; Jiang, F. Coal mine safety accident analysis based on Apriori algorithm. J. Saf. Environ. 2024 , 6 , 1–9. [ Google Scholar ]
• Hu, S.; Yang, B.; Weng, J.; Zhou, W. A cause analysis of residents’ dependence on public transportation based on association rules. J. Transp. Inf. Saf. 2023 , 41 , 147–156. [ Google Scholar ]
• Zhang, Z.; Xue, J.; Chen, G. Sentiment Analysis of Class Imbalance Data Under the Framework of Deep Learning. J. Mod. Inf. 2021 , 41 , 75–82. [ Google Scholar ]
• Xu, X.; Tian, K. A novel financial text sentiment analysis-based approach for stock index prediction. J. Quant. Technol. Econ. 2021 , 38 , 124–145. [ Google Scholar ]
• Hoel, A.; Dahl, T.I. Why bother? Student motivation to participate in student evaluations of teaching. Assess. Eval. Higher Educ. 2018 , 44 , 361–378. [ Google Scholar ] [ CrossRef ]

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Share and Cite

Qi, F.; Gao, Y.; Wang, M.; Jiang, T.; Li, Z. Data Mining of Online Teaching Evaluation Based on Deep Learning. Mathematics 2024 , 12 , 2692. https://doi.org/10.3390/math12172692

Qi F, Gao Y, Wang M, Jiang T, Li Z. Data Mining of Online Teaching Evaluation Based on Deep Learning. Mathematics . 2024; 12(17):2692. https://doi.org/10.3390/math12172692

Qi, Fenghua, Yuxuan Gao, Meiling Wang, Tao Jiang, and Zhenhuan Li. 2024. "Data Mining of Online Teaching Evaluation Based on Deep Learning" Mathematics 12, no. 17: 2692. https://doi.org/10.3390/math12172692

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