online learning research paper

Journal of Online Learning Research (JOLR)

Issn# 2374-1473.

The Journal of Online Learning Research (JOLR) is a peer-reviewed journal devoted to the theoretical, empirical, and pragmatic understanding of technologies and their impact on pedagogy and policy in primary and secondary (K-12) online and blended environments.  

Three issues are published annually. Each submitted manuscript goes through a rigorous blind peer review process. If accepted, article is then published in either the general research or international section. Additional information for each section is found below. 

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Articles focus on research related to K-12 online and blended learning. Research articles can:

• Address online learning, catering particularly to the educators who research, practice, design, and/or administer in primary and secondary schooling in online settings. However, the journal also serves those educators who have chosen to blend online learning tools and strategies in their face-to-face classroom.
• Include qualitative, quantitative, and mixed methods research from multiple fields and disciplines that have a shared goal of improving primary and secondary education worldwide.

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Articles focus on research related to online and blended learning with primary and secondary students in international contexts. Articles can include: 

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Practitioner’s personal experiences with teaching and learning can provide valuable information about the contexts to which some researchers expect their findings to apply. Articles in the Practitioner Corner section should present detailed explanations and reflections on educational innovations. Ideally, these articles document problems posed in specific contexts, strategies tried, outcomes, and reflections on learning. Taken together, these articles should reveal trends in educational needs and everyday factors that influence K-12 distance, online, and blended learning. Articles in the Practitioner Corner section should go beyond “Did it work?” to explore how interventions function and the boundaries of their scalability (i.e., how could it be or what is stopping it from being implemented elsewhere?).

Articles in the Practitioner Corner section should contain a structured abstract using the format presented below. The body of the manuscript need not conform to the structure of the abstract.

• Context. Briefly summarize the context in which the intervention was implemented.
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Articles in the Practitioner Corner section must be at least 1000 words but should be no more than 3500 words. Articles submitted to the Practitioner Corner section will not be sent through a traditional blind review process but will undergo an editorial review or a review by a topical expert.

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Authors should aim for 1000-1500 words.

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• Published: 09 January 2024

Online vs in-person learning in higher education: effects on student achievement and recommendations for leadership

• Bandar N. Alarifi 1 &
• Steve Song 2  

Humanities and Social Sciences Communications volume  11 , Article number:  86 ( 2024 ) Cite this article

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• Science, technology and society

This study is a comparative analysis of online distance learning and traditional in-person education at King Saud University in Saudi Arabia, with a focus on understanding how different educational modalities affect student achievement. The justification for this study lies in the rapid shift towards online learning, especially highlighted by the educational changes during the COVID-19 pandemic. By analyzing the final test scores of freshman students in five core courses over the 2020 (in-person) and 2021 (online) academic years, the research provides empirical insights into the efficacy of online versus traditional education. Initial observations suggested that students in online settings scored lower in most courses. However, after adjusting for variables like gender, class size, and admission scores using multiple linear regression, a more nuanced picture emerged. Three courses showed better performance in the 2021 online cohort, one favored the 2020 in-person group, and one was unaffected by the teaching format. The study emphasizes the crucial need for a nuanced, data-driven strategy in integrating online learning within higher education systems. It brings to light the fact that the success of educational methodologies is highly contingent on specific contextual factors. This finding advocates for educational administrators and policymakers to exercise careful and informed judgment when adopting online learning modalities. It encourages them to thoroughly evaluate how different subjects and instructional approaches might interact with online formats, considering the variable effects these might have on learning outcomes. This approach ensures that decisions about implementing online education are made with a comprehensive understanding of its diverse and context-specific impacts, aiming to optimize educational effectiveness and student success.

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Introduction.

The year 2020 marked an extraordinary period, characterized by the global disruption caused by the COVID-19 pandemic. Governments and institutions worldwide had to adapt to unforeseen challenges across various domains, including health, economy, and education. In response, many educational institutions quickly transitioned to distance teaching (also known as e-learning, online learning, or virtual classrooms) to ensure continued access to education for their students. However, despite this rapid and widespread shift to online learning, a comprehensive examination of its effects on student achievement in comparison to traditional in-person instruction remains largely unexplored.

In research examining student outcomes in the context of online learning, the prevailing trend is the consistent observation that online learners often achieve less favorable results when compared to their peers in traditional classroom settings (e.g., Fischer et al., 2020 ; Bettinger et al., 2017 ; Edvardsson and Oskarsson, 2008 ). However, it is important to note that a significant portion of research on online learning has primarily focused on its potential impact (Kuhfeld et al., 2020 ; Azevedo et al., 2020 ; Di Pietro et al., 2020 ) or explored various perspectives (Aucejo et al., 2020 ; Radha et al., 2020 ) concerning distance education. These studies have often omitted a comprehensive and nuanced examination of its concrete academic consequences, particularly in terms of test scores and grades.

Given the dearth of research on the academic impact of online learning, especially in light of Covid-19 in the educational arena, the present study aims to address that gap by assessing the effectiveness of distance learning compared to in-person teaching in five required freshmen-level courses at King Saud University, Saudi Arabia. To accomplish this objective, the current study compared the final exam results of 8297 freshman students who were enrolled in the five courses in person in 2020 to their 8425 first-year counterparts who has taken the same courses at the same institution in 2021 but in an online format.

The final test results of the five courses (i.e., University Skills 101, Entrepreneurship 101, Computer Skills 101, Computer Skills 101, and Fitness and Health Culture 101) were examined, accounting for potential confounding factors such as gender, class size and admission scores, which have been cited in past research to be correlated with student achievement (e.g., Meinck and Brese, 2019 ; Jepsen, 2015 ) Additionally, as the preparatory year at King Saud University is divided into five tracks—health, nursing, science, business, and humanity, the study classified students based on their respective disciplines.

Motivation for the study

The rapid expansion of distance learning in higher education, particularly highlighted during the recent COVID-19 pandemic (Volk et al., 2020 ; Bettinger et al., 2017 ), underscores the need for alternative educational approaches during crises. Such disruptions can catalyze innovation and the adoption of distance learning as a contingency plan (Christensen et al., 2015 ). King Saud University, like many institutions worldwide, faced the challenge of transitioning abruptly to online learning in response to the pandemic.

E-learning has gained prominence in higher education due to technological advancements, offering institutions a competitive edge (Valverde-Berrocoso et al., 2020 ). Especially during conditions like the COVID-19 pandemic, electronic communication was utilized across the globe as a feasible means to overcome barriers and enhance interactions (Bozkurt, 2019 ).

Distance learning, characterized by flexibility, became crucial when traditional in-person classes are hindered by unforeseen circumstance such as the ones posed by COVID-19 (Arkorful and Abaidoo, 2015 ). Scholars argue that it allows students to learn at their own pace, often referred to as self-directed learning (Hiemstra, 1994 ) or self-education (Gadamer, 2001 ). Additional advantages include accessibility, cost-effectiveness, and flexibility (Sadeghi, 2019 ).

However, distance learning is not immune to its own set of challenges. Technical impediments, encompassing network issues, device limitations, and communication hiccups, represent formidable hurdles (Sadeghi, 2019 ). Furthermore, concerns about potential distractions in the online learning environment, fueled by the ubiquity of the internet and social media, have surfaced (Hall et al., 2020 ; Ravizza et al., 2017 ). The absence of traditional face-to-face interactions among students and between students and instructors is also viewed as a potential drawback (Sadeghi, 2019 ).

Given the evolving understanding of the pros and cons of distance learning, this study aims to contribute to the existing literature by assessing the effectiveness of distance learning, specifically in terms of student achievement, as compared to in-person classroom learning at King Saud University, one of Saudi Arabia’s largest higher education institutions.

Academic achievement: in-person vs online learning

The primary driving force behind the rapid integration of technology in education has been its emphasis on student performance (Lai and Bower, 2019 ). Over the past decade, numerous studies have undertaken comparisons of student academic achievement in online and in-person settings (e.g., Bettinger et al., 2017 ; Fischer et al., 2020 ; Iglesias-Pradas et al., 2021 ). This section offers a concise review of the disparities in academic achievement between college students engaged in in-person and online learning, as identified in existing research.

A number of studies point to the superiority of traditional in-person education over online learning in terms of academic outcomes. For example, Fischer et al. ( 2020 ) conducted a comprehensive study involving 72,000 university students across 433 subjects, revealing that online students tend to achieve slightly lower academic results than their in-class counterparts. Similarly, Bettinger et al. ( 2017 ) found that students at for-profit online universities generally underperformed when compared to their in-person peers. Supporting this trend, Figlio et al. ( 2013 ) indicated that in-person instruction consistently produced better results, particularly among specific subgroups like males, lower-performing students, and Hispanic learners. Additionally, Kaupp’s ( 2012 ) research in California community colleges demonstrated that online students faced lower completion and success rates compared to their traditional in-person counterparts (Fig. 1 ).

The figure compared student achievement in the final tests in the five courses by year, using independent-samples t-tests; the results show a statistically-significant drop in test scores from 2020 (in person) to 2021 (online) for all courses except CT_101.

In contrast, other studies present evidence of online students outperforming their in-person peers. For example, Iglesias-Pradas et al. ( 2021 ) conducted a comparative analysis of 43 bachelor courses at Telecommunication Engineering College in Malaysia, revealing that online students achieved higher academic outcomes than their in-person counterparts. Similarly, during the COVID-19 pandemic, Gonzalez et al. ( 2020 ) found that students engaged in online learning performed better than those who had previously taken the same subjects in traditional in-class settings.

Expanding on this topic, several studies have reported mixed results when comparing the academic performance of online and in-person students, with various student and instructor factors emerging as influential variables. Chesser et al. ( 2020 ) noted that student traits such as conscientiousness, agreeableness, and extraversion play a substantial role in academic achievement, regardless of the learning environment—be it traditional in-person classrooms or online settings. Furthermore, Cacault et al. ( 2021 ) discovered that online students with higher academic proficiency tend to outperform those with lower academic capabilities, suggesting that differences in students’ academic abilities may impact their performance. In contrast, Bergstrand and Savage ( 2013 ) found that online classes received lower overall ratings and exhibited a less respectful learning environment when compared to in-person instruction. Nevertheless, they also observed that the teaching efficiency of both in-class and online courses varied significantly depending on the instructors’ backgrounds and approaches. These findings underscore the multifaceted nature of the online vs. in-person learning debate, highlighting the need for a nuanced understanding of the factors at play.

Theoretical framework

Constructivism is a well-established learning theory that places learners at the forefront of their educational experience, emphasizing their active role in constructing knowledge through interactions with their environment (Duffy and Jonassen, 2009 ). According to constructivist principles, learners build their understanding by assimilating new information into their existing cognitive frameworks (Vygotsky, 1978 ). This theory highlights the importance of context, active engagement, and the social nature of learning (Dewey, 1938 ). Constructivist approaches often involve hands-on activities, problem-solving tasks, and opportunities for collaborative exploration (Brooks and Brooks, 1999 ).

In the realm of education, subject-specific pedagogy emerges as a vital perspective that acknowledges the distinctive nature of different academic disciplines (Shulman, 1986 ). It suggests that teaching methods should be tailored to the specific characteristics of each subject, recognizing that subjects like mathematics, literature, or science require different approaches to facilitate effective learning (Shulman, 1987 ). Subject-specific pedagogy emphasizes that the methods of instruction should mirror the ways experts in a particular field think, reason, and engage with their subject matter (Cochran-Smith and Zeichner, 2005 ).

When applying these principles to the design of instruction for online and in-person learning environments, the significance of adapting methods becomes even more pronounced. Online learning often requires unique approaches due to its reliance on technology, asynchronous interactions, and potential for reduced social presence (Anderson, 2003 ). In-person learning, on the other hand, benefits from face-to-face interactions and immediate feedback (Allen and Seaman, 2016 ). Here, the interplay of constructivism and subject-specific pedagogy becomes evident.

Online learning. In an online environment, constructivist principles can be upheld by creating interactive online activities that promote exploration, reflection, and collaborative learning (Salmon, 2000 ). Discussion forums, virtual labs, and multimedia presentations can provide opportunities for students to actively engage with the subject matter (Harasim, 2017 ). By integrating subject-specific pedagogy, educators can design online content that mirrors the discipline’s methodologies while leveraging technology for authentic experiences (Koehler and Mishra, 2009 ). For instance, an online history course might incorporate virtual museum tours, primary source analysis, and collaborative timeline projects.

In-person learning. In a traditional brick-and-mortar classroom setting, constructivist methods can be implemented through group activities, problem-solving tasks, and in-depth discussions that encourage active participation (Jonassen et al., 2003 ). Subject-specific pedagogy complements this by shaping instructional methods to align with the inherent characteristics of the subject (Hattie, 2009). For instance, in a physics class, hands-on experiments and real-world applications can bring theoretical concepts to life (Hake, 1998 ).

In sum, the fusion of constructivism and subject-specific pedagogy offers a versatile approach to instructional design that adapts to different learning environments (Garrison, 2011 ). By incorporating the principles of both theories, educators can tailor their methods to suit the unique demands of online and in-person learning, ultimately providing students with engaging and effective learning experiences that align with the nature of the subject matter and the mode of instruction.

Course description

The Self-Development Skills Department at King Saud University (KSU) offers five mandatory freshman-level courses. These courses aim to foster advanced thinking skills and cultivate scientific research abilities in students. They do so by imparting essential skills, identifying higher-level thinking patterns, and facilitating hands-on experience in scientific research. The design of these classes is centered around aiding students’ smooth transition into university life. Brief descriptions of these courses are as follows:

University Skills 101 (CI 101) is a three-hour credit course designed to nurture essential academic, communication, and personal skills among all preparatory year students at King Saud University. The primary goal of this course is to equip students with the practical abilities they need to excel in their academic pursuits and navigate their university lives effectively. CI 101 comprises 12 sessions and is an integral part of the curriculum for all incoming freshmen, ensuring a standardized foundation for skill development.

Fitness and Health 101 (FAJB 101) is a one-hour credit course. FAJB 101 focuses on the aspects of self-development skills in terms of health and physical, and the skills related to personal health, nutrition, sports, preventive, psychological, reproductive, and first aid. This course aims to motivate students’ learning process through entertainment, sports activities, and physical exercises to maintain their health. This course is required for all incoming freshmen students at King Saud University.

Entrepreneurship 101 (ENT 101) is a one-hour- credit course. ENT 101 aims to develop students’ skills related to entrepreneurship. The course provides students with knowledge and skills to generate and transform ideas and innovations into practical commercial projects in business settings. The entrepreneurship course consists of 14 sessions and is taught only to students in the business track.

Computer Skills 101 (CT 101) is a three-hour credit course. This provides students with the basic computer skills, e.g., components, operating systems, applications, and communication backup. The course explores data visualization, introductory level of modern programming with algorithms and information security. CT 101 course is taught for all tracks except those in the human track.

Computer Skills 102 (CT 102) is a three-hour credit course. It provides IT skills to the students to utilize computers with high efficiency, develop students’ research and scientific skills, and increase capability to design basic educational software. CT 102 course focuses on operating systems such as Microsoft Office. This course is only taught for students in the human track.

Structure and activities

These courses ranged from one to three hours. A one-hour credit means that students must take an hour of the class each week during the academic semester. The same arrangement would apply to two and three credit-hour courses. The types of activities in each course are shown in Table 1 .

At King Saud University, each semester spans 15 weeks in duration. The total number of semester hours allocated to each course serves as an indicator of its significance within the broader context of the academic program, including the diverse tracks available to students. Throughout the two years under study (i.e., 2020 and 2021), course placements (fall or spring), course content, and the organizational structure remained consistent and uniform.

Participants

The study’s data comes from test scores of a cohort of 16,722 first-year college students enrolled at King Saud University in Saudi Arabia over the span of two academic years: 2020 and 2021. Among these students, 8297 were engaged in traditional, in-person learning in 2020, while 8425 had transitioned to online instruction for the same courses in 2021 due to the Covid-19 pandemic. In 2020, the student population consisted of 51.5% females and 48.5% males. However, in 2021, there was a reversal in these proportions, with female students accounting for 48.5% and male students comprising 51.5% of the total participants.

Regarding student enrollment in the five courses, Table 2 provides a detailed breakdown by average class size, admission scores, and the number of students enrolled in the courses during the two years covered by this study. While the total number of students in each course remained relatively consistent across the two years, there were noticeable fluctuations in average class sizes. Specifically, four out of the five courses experienced substantial increases in class size, with some nearly doubling in size (e.g., ENT_101 and CT_102), while one course (CT_101) showed a reduction in its average class size.

In this study, it must be noted that while some students enrolled in up to three different courses within the same academic year, none repeated the same exam in both years. Specifically, students who failed to pass their courses in 2020 were required to complete them in summer sessions and were consequently not included in this study’s dataset. To ensure clarity and precision in our analysis, the research focused exclusively on student test scores to evaluate and compare the academic effectiveness of online and traditional in-person learning methods. This approach was chosen to provide a clear, direct comparison of the educational impacts associated with each teaching format.

Descriptive analysis of the final exam scores for the two years (2020 and 2021) were conducted. Additionally, comparison of student outcomes in in-person classes in 2020 to their online platform peers in 2021 were conducted using an independent-samples t -test. Subsequently, in order to address potential disparities between the two groups arising from variables such as gender, class size, and admission scores (which serve as an indicator of students’ academic aptitude and pre-enrollment knowledge), multiple regression analyses were conducted. In these multivariate analyses, outcomes of both in-person and online cohorts were assessed within their respective tracks. By carefully considering essential aforementioned variables linked to student performance, the study aimed to ensure a comprehensive and equitable evaluation.

Study instrument

The study obtained students’ final exam scores for the years 2020 (in-person) and 2021 (online) from the school’s records office through their examination management system. In the preparatory year at King Saud University, final exams for all courses are developed by committees composed of faculty members from each department. To ensure valid comparisons, the final exam questions, crafted by departmental committees of professors, remained consistent and uniform for the two years under examination.

Table 3 provides a comprehensive assessment of the reliability of all five tests included in our analysis. These tests exhibit a strong degree of internal consistency, with Cronbach’s alpha coefficients spanning a range from 0.77 to 0.86. This robust and consistent internal consistency measurement underscores the dependable nature of these tests, affirming their reliability and suitability for the study’s objectives.

In terms of assessing test validity, content validity was ensured through a thorough review by university subject matter experts, resulting in test items that align well with the content domain and learning objectives. Additionally, criterion-related validity was established by correlating students’ admissions test scores with their final required freshman test scores in the five subject areas, showing a moderate and acceptable relationship (0.37 to 0.56) between the test scores and the external admissions test. Finally, construct validity was confirmed through reviews by experienced subject instructors, leading to improvements in test content. With guidance from university subject experts, construct validity was established, affirming the effectiveness of the final tests in assessing students’ subject knowledge at the end of their coursework.

Collectively, these validity and reliability measures affirm the soundness and integrity of the final subject tests, establishing their suitability as effective assessment tools for evaluating students’ knowledge in their five mandatory freshman courses at King Saud University.

After obtaining research approval from the Research Committee at King Saud University, the coordinators of the five courses (CI_101, ENT_101, CT_101, CT_102, and FAJB_101) supplied the researchers with the final exam scores of all first-year preparatory year students at King Saud University for the initial semester of the academic years 2020 and 2021. The sample encompassed all students who had completed these five courses during both years, resulting in a total of 16,722 students forming the final group of participants.

Limitations

Several limitations warrant acknowledgment in this study. First, the research was conducted within a well-resourced major public university. As such, the experiences with online classes at other types of institutions (e.g., community colleges, private institutions) may vary significantly. Additionally, the limited data pertaining to in-class teaching practices and the diversity of learning activities across different courses represents a gap that could have provided valuable insights for a more thorough interpretation and explanation of the study’s findings.

To compare student achievement in the final tests in the five courses by year, independent-samples t -tests were conducted. Table 4 shows a statistically-significant drop in test scores from 2020 (in person) to 2021 (online) for all courses except CT_101. The biggest decline was with CT_102 with 3.58 points, and the smallest decline was with CI_101 with 0.18 points.

However, such simple comparison of means between the two years (via t -tests) by subjects does not account for the differences in gender composition, class size, and admission scores between the two academic years, all of which have been associated with student outcomes (e.g., Ho and Kelman, 2014 ; De Paola et al., 2013 ). To account for such potential confounding variables, multiple regressions were conducted to compare the 2 years’ results while controlling for these three factors associated with student achievement.

Table 5 presents the regression results, illustrating the variation in final exam scores between 2020 and 2021, while controlling for gender, class size, and admission scores. Importantly, these results diverge significantly from the outcomes obtained through independent-sample t -test analyses.

Taking into consideration the variables mentioned earlier, students in the 2021 online cohort demonstrated superior performance compared to their 2020 in-person counterparts in CI_101, FAJB_101, and CT_101, with score advantages of 0.89, 0.56, and 5.28 points, respectively. Conversely, in the case of ENT_101, online students in 2021 scored 0.69 points lower than their 2020 in-person counterparts. With CT_102, there were no statistically significant differences in final exam scores between the two cohorts of students.

The study sought to assess the effectiveness of distance learning compared to in-person learning in the higher education setting in Saudi Arabia. We analyzed the final exam scores of 16,722 first-year college students in King Saud University in five required subjects (i.e., CI_101, ENT_101, CT_101, CT_102, and FAJB_101). The study initially performed a simple comparison of mean scores by tracks by year (via t -tests) and then a number of multiple regression analyses which controlled for class size, gender composition, and admission scores.

Overall, the study’s more in-depth findings using multiple regression painted a wholly different picture than the results obtained using t -tests. After controlling for class size, gender composition, and admissions scores, online students in 2021 performed better than their in-person instruction peers in 2020 in University Skills (CI_101), Fitness and Health (FAJB_101), and Computer Skills (CT_101), whereas in-person students outperformed their online peers in Entrepreneurship (ENT_101). There was no meaningful difference in outcomes for students in the Computer Skills (CT_102) course for the two years.

In light of these findings, it raises the question: why do we observe minimal differences (less than a one-point gain or loss) in student outcomes in courses like University Skills, Fitness and Health, Entrepreneurship, and Advanced Computer Skills based on the mode of instruction? Is it possible that when subjects are primarily at a basic or introductory level, as is the case with these courses, the mode of instruction may have a limited impact as long as the concepts are effectively communicated in a manner familiar and accessible to students?

In today’s digital age, one could argue that students in more developed countries, such as Saudi Arabia, generally possess the skills and capabilities to effectively engage with materials presented in both in-person and online formats. However, there is a notable exception in the Basic Computer Skills course, where the online cohort outperformed their in-person counterparts by more than 5 points. Insights from interviews with the instructors of this course suggest that this result may be attributed to the course’s basic and conceptual nature, coupled with the availability of instructional videos that students could revisit at their own pace.

Given that students enter this course with varying levels of computer skills, self-paced learning may have allowed them to cover course materials at their preferred speed, concentrating on less familiar topics while swiftly progressing through concepts they already understood. The advantages of such self-paced learning have been documented by scholars like Tullis and Benjamin ( 2011 ), who found that self-paced learners often outperform those who spend the same amount of time studying identical materials. This approach allows learners to allocate their time more effectively according to their individual learning pace, providing greater ownership and control over their learning experience. As such, in courses like introductory computer skills, it can be argued that becoming familiar with fundamental and conceptual topics may not require extensive in-class collaboration. Instead, it may be more about exposure to and digestion of materials in a format and at a pace tailored to students with diverse backgrounds, knowledge levels, and skill sets.

Further investigation is needed to more fully understand why some classes benefitted from online instruction while others did not, and vice versa. Perhaps, it could be posited that some content areas are more conducive to in-person (or online) format while others are not. Or it could be that the different results of the two modes of learning were driven by students of varying academic abilities and engagement, with low-achieving students being more vulnerable to the limitations of online learning (e.g., Kofoed et al., 2021 ). Whatever the reasons, the results of the current study can be enlightened by a more in-depth analysis of the various factors associated with such different forms of learning. Moreover, although not clear cut, what the current study does provide is additional evidence against any dire consequences to student learning (at least in the higher ed setting) as a result of sudden increase in online learning with possible benefits of its wider use being showcased.

Based on the findings of this study, we recommend that educational leaders adopt a measured approach to online learning—a stance that neither fully embraces nor outright denounces it. The impact on students’ experiences and engagement appears to vary depending on the subjects and methods of instruction, sometimes hindering, other times promoting effective learning, while some classes remain relatively unaffected.

Rather than taking a one-size-fits-all approach, educational leaders should be open to exploring the nuances behind these outcomes. This involves examining why certain courses thrived with online delivery, while others either experienced a decline in student achievement or remained largely unaffected. By exploring these differentiated outcomes associated with diverse instructional formats, leaders in higher education institutions and beyond can make informed decisions about resource allocation. For instance, resources could be channeled towards in-person learning for courses that benefit from it, while simultaneously expanding online access for courses that have demonstrated improved outcomes through its virtual format. This strategic approach not only optimizes resource allocation but could also open up additional revenue streams for the institution.

Considering the enduring presence of online learning, both before the pandemic and its accelerated adoption due to Covid-19, there is an increasing need for institutions of learning and scholars in higher education, as well as other fields, to prioritize the study of its effects and optimal utilization. This study, which compares student outcomes between two cohorts exposed to in-person and online instruction (before and during Covid-19) at the largest university in Saudi Arabia, represents a meaningful step in this direction.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Alarifi, B.N., Song, S. Online vs in-person learning in higher education: effects on student achievement and recommendations for leadership. Humanit Soc Sci Commun 11 , 86 (2024). https://doi.org/10.1057/s41599-023-02590-1

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Impact of online classes on the satisfaction and performance of students during the pandemic period of COVID 19

• Published: 21 April 2021
• Volume 26 , pages 6923–6947, ( 2021 )

Cite this article

• Ram Gopal 1 ,
• Varsha Singh 1 &
• Arun Aggarwal   ORCID: orcid.org/0000-0003-3986-188X 2  

The aim of the study is to identify the factors affecting students’ satisfaction and performance regarding online classes during the pandemic period of COVID–19 and to establish the relationship between these variables. The study is quantitative in nature, and the data were collected from 544 respondents through online survey who were studying the business management (B.B.A or M.B.A) or hotel management courses in Indian universities. Structural equation modeling was used to analyze the proposed hypotheses. The results show that four independent factors used in the study viz. quality of instructor, course design, prompt feedback, and expectation of students positively impact students’ satisfaction and further student’s satisfaction positively impact students’ performance. For educational management, these four factors are essential to have a high level of satisfaction and performance for online courses. This study is being conducted during the epidemic period of COVID- 19 to check the effect of online teaching on students’ performance.

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1 Introduction

Coronavirus is a group of viruses that is the main root of diseases like cough, cold, sneezing, fever, and some respiratory symptoms (WHO, 2019 ). Coronavirus is a contagious disease, which is spreading very fast amongst the human beings. COVID-19 is a new sprain which was originated in Wuhan, China, in December 2019. Coronavirus circulates in animals, but some of these viruses can transmit between animals and humans (Perlman & Mclntosh, 2020 ). As of March 282,020, according to the MoHFW, a total of 909 confirmed COVID-19 cases (862 Indians and 47 foreign nationals) had been reported in India (Centers for Disease Control and Prevention, 2020 ). Officially, no vaccine or medicine is evaluated to cure the spread of COVID-19 (Yu et al., 2020 ). The influence of the COVID-19 pandemic on the education system leads to schools and colleges’ widespread closures worldwide. On March 24, India declared a country-wide lockdown of schools and colleges (NDTV, 2020 ) for preventing the transmission of the coronavirus amongst the students (Bayham & Fenichel, 2020 ). School closures in response to the COVID-19 pandemic have shed light on several issues affecting access to education. COVID-19 is soaring due to which the huge number of children, adults, and youths cannot attend schools and colleges (UNESCO, 2020 ). Lah and Botelho ( 2012 ) contended that the effect of school closing on students’ performance is hazy.

Similarly, school closing may also affect students because of disruption of teacher and students’ networks, leading to poor performance. Bridge ( 2020 ) reported that schools and colleges are moving towards educational technologies for student learning to avoid a strain during the pandemic season. Hence, the present study’s objective is to develop and test a conceptual model of student’s satisfaction pertaining to online teaching during COVID-19, where both students and teachers have no other option than to use the online platform uninterrupted learning and teaching.

UNESCO recommends distance learning programs and open educational applications during school closure caused by COVID-19 so that schools and teachers use to teach their pupils and bound the interruption of education. Therefore, many institutes go for the online classes (Shehzadi et al., 2020 ).

As a versatile platform for learning and teaching processes, the E-learning framework has been increasingly used (Salloum & Shaalan, 2018 ). E-learning is defined as a new paradigm of online learning based on information technology (Moore et al., 2011 ). In contrast to traditional learning academics, educators, and other practitioners are eager to know how e-learning can produce better outcomes and academic achievements. Only by analyzing student satisfaction and their performance can the answer be sought.

Many comparative studies have been carried out to prove the point to explore whether face-to-face or traditional teaching methods are more productive or whether online or hybrid learning is better (Lockman & Schirmer, 2020 ; Pei & Wu, 2019 ; González-Gómez et al., 2016 ; González-Gómez et al., 2016 ). Results of the studies show that the students perform much better in online learning than in traditional learning. Henriksen et al. ( 2020 ) highlighted the problems faced by educators while shifting from offline to online mode of teaching. In the past, several research studies had been carried out on online learning to explore student satisfaction, acceptance of e-learning, distance learning success factors, and learning efficiency (Sher, 2009 ; Lee, 2014 ; Yen et al., 2018 ). However, scant amount of literature is available on the factors that affect the students’ satisfaction and performance in online classes during the pandemic of Covid-19 (Rajabalee & Santally, 2020 ). In the present study, the authors proposed that course design, quality of the instructor, prompt feedback, and students’ expectations are the four prominent determinants of learning outcome and satisfaction of the students during online classes (Lee, 2014 ).

The Course Design refers to curriculum knowledge, program organization, instructional goals, and course structure (Wright, 2003 ). If well planned, course design increasing the satisfaction of pupils with the system (Almaiah & Alyoussef, 2019 ). Mtebe and Raisamo ( 2014 ) proposed that effective course design will help in improving the performance through learners knowledge and skills (Khan & Yildiz, 2020 ; Mohammed et al., 2020 ). However, if the course is not designed effectively then it might lead to low usage of e-learning platforms by the teachers and students (Almaiah & Almulhem, 2018 ). On the other hand, if the course is designed effectively then it will lead to higher acceptance of e-learning system by the students and their performance also increases (Mtebe & Raisamo, 2014 ). Hence, to prepare these courses for online learning, many instructors who are teaching blended courses for the first time are likely to require a complete overhaul of their courses (Bersin, 2004 ; Ho et al., 2006 ).

The second-factor, Instructor Quality, plays an essential role in affecting the students’ satisfaction in online classes. Instructor quality refers to a professional who understands the students’ educational needs, has unique teaching skills, and understands how to meet the students’ learning needs (Luekens et al., 2004 ). Marsh ( 1987 ) developed five instruments for measuring the instructor’s quality, in which the main method was Students’ Evaluation of Educational Quality (SEEQ), which delineated the instructor’s quality. SEEQ is considered one of the methods most commonly used and embraced unanimously (Grammatikopoulos et al., 2014 ). SEEQ was a very useful method of feedback by students to measure the instructor’s quality (Marsh, 1987 ).

The third factor that improves the student’s satisfaction level is prompt feedback (Kinicki et al., 2004 ). Feedback is defined as information given by lecturers and tutors about the performance of students. Within this context, feedback is a “consequence of performance” (Hattie & Timperley, 2007 , p. 81). In education, “prompt feedback can be described as knowing what you know and what you do not related to learning” (Simsek et al., 2017 , p.334). Christensen ( 2014 ) studied linking feedback to performance and introduced the positivity ratio concept, which is a mechanism that plays an important role in finding out the performance through feedback. It has been found that prompt feedback helps in developing a strong linkage between faculty and students which ultimately leads to better learning outcomes (Simsek et al., 2017 ; Chang, 2011 ).

The fourth factor is students’ expectation . Appleton-Knapp and Krentler ( 2006 ) measured the impact of student’s expectations on their performance. They pin pointed that the student expectation is important. When the expectations of the students are achieved then it lead to the higher satisfaction level of the student (Bates & Kaye, 2014 ). These findings were backed by previous research model “Student Satisfaction Index Model” (Zhang et al., 2008 ). However, when the expectations are students is not fulfilled then it might lead to lower leaning and satisfaction with the course. Student satisfaction is defined as students’ ability to compare the desired benefit with the observed effect of a particular product or service (Budur et al., 2019 ). Students’ whose grade expectation is high will show high satisfaction instead of those facing lower grade expectations.

The scrutiny of the literature show that although different researchers have examined the factors affecting student satisfaction but none of the study has examined the effect of course design, quality of the instructor, prompt feedback, and students’ expectations on students’ satisfaction with online classes during the pandemic period of Covid-19. Therefore, this study tries to explore the factors that affect students’ satisfaction and performance regarding online classes during the pandemic period of COVID–19. As the pandemic compelled educational institutions to move online with which they were not acquainted, including teachers and learners. The students were not mentally prepared for such a shift. Therefore, this research will be examined to understand what factors affect students and how students perceived these changes which are reflected through their satisfaction level.

This paper is structured as follows: The second section provides a description of theoretical framework and the linkage among different research variables and accordingly different research hypotheses were framed. The third section deals with the research methodology of the paper as per APA guideline. The outcomes and corresponding results of the empirical analysis are then discussed. Lastly, the paper concludes with a discussion and proposes implications for future studies.

2 Theoretical framework

Achievement goal theory (AGT) is commonly used to understand the student’s performance, and it is proposed by four scholars Carole Ames, Carol Dweck, Martin Maehr, and John Nicholls in the late 1970s (Elliot, 2005 ). Elliott & Dweck ( 1988 , p11) define that “an achievement goal involves a program of cognitive processes that have cognitive, affective and behavioral consequence”. This theory suggests that students’ motivation and achievement-related behaviors can be easily understood by the purpose and the reasons they adopted while they are engaged in the learning activities (Dweck & Leggett, 1988 ; Ames, 1992 ; Urdan, 1997 ). Some of the studies believe that there are four approaches to achieve a goal, i.e., mastery-approach, mastery avoidance, performance approach, and performance-avoidance (Pintrich, 1999 ; Elliot & McGregor, 2001 ; Schwinger & Stiensmeier-Pelster, 2011 , Hansen & Ringdal, 2018 ; Mouratidis et al., 2018 ). The environment also affects the performance of students (Ames & Archer, 1988 ). Traditionally, classroom teaching is an effective method to achieve the goal (Ames & Archer, 1988 ; Ames, 1992 ; Clayton et al., 2010 ) however in the modern era, the internet-based teaching is also one of the effective tools to deliver lectures, and web-based applications are becoming modern classrooms (Azlan et al., 2020 ). Hence, following section discuss about the relationship between different independent variables and dependent variables (Fig. 1 ).

Proposed Model

3 Hypotheses development

3.1 quality of the instructor and satisfaction of the students.

Quality of instructor with high fanaticism on student’s learning has a positive impact on their satisfaction. Quality of instructor is one of the most critical measures for student satisfaction, leading to the education process’s outcome (Munteanu et al., 2010 ; Arambewela & Hall, 2009 ; Ramsden, 1991 ). Suppose the teacher delivers the course effectively and influence the students to do better in their studies. In that case, this process leads to student satisfaction and enhances the learning process (Ladyshewsky, 2013 ). Furthermore, understanding the need of learner by the instructor also ensures student satisfaction (Kauffman, 2015 ). Hence the hypothesis that the quality of instructor significantly affects the satisfaction of the students was included in this study.

H1: The quality of the instructor positively affects the satisfaction of the students.

3.2 Course design and satisfaction of students

The course’s technological design is highly persuading the students’ learning and satisfaction through their course expectations (Liaw, 2008 ; Lin et al., 2008 ). Active course design indicates the students’ effective outcomes compared to the traditional design (Black & Kassaye, 2014 ). Learning style is essential for effective course design (Wooldridge, 1995 ). While creating an online course design, it is essential to keep in mind that we generate an experience for students with different learning styles. Similarly, (Jenkins, 2015 ) highlighted that the course design attributes could be developed and employed to enhance student success. Hence the hypothesis that the course design significantly affects students’ satisfaction was included in this study.

H2: Course design positively affects the satisfaction of students.

3.3 Prompt feedback and satisfaction of students

The emphasis in this study is to understand the influence of prompt feedback on satisfaction. Feedback gives the information about the students’ effective performance (Chang, 2011 ; Grebennikov & Shah, 2013 ; Simsek et al., 2017 ). Prompt feedback enhances student learning experience (Brownlee et al., 2009 ) and boosts satisfaction (O'donovan, 2017 ). Prompt feedback is the self-evaluation tool for the students (Rogers, 1992 ) by which they can improve their performance. Eraut ( 2006 ) highlighted the impact of feedback on future practice and student learning development. Good feedback practice is beneficial for student learning and teachers to improve students’ learning experience (Yorke, 2003 ). Hence the hypothesis that prompt feedback significantly affects satisfaction was included in this study.

H3: Prompt feedback of the students positively affects the satisfaction.

3.4 Expectations and satisfaction of students

Expectation is a crucial factor that directly influences the satisfaction of the student. Expectation Disconfirmation Theory (EDT) (Oliver, 1980 ) was utilized to determine the level of satisfaction based on their expectations (Schwarz & Zhu, 2015 ). Student’s expectation is the best way to improve their satisfaction (Brown et al., 2014 ). It is possible to recognize student expectations to progress satisfaction level (ICSB, 2015 ). Finally, the positive approach used in many online learning classes has been shown to place a high expectation on learners (Gold, 2011 ) and has led to successful outcomes. Hence the hypothesis that expectations of the student significantly affect the satisfaction was included in this study.

H4: Expectations of the students positively affects the satisfaction.

3.5 Satisfaction and performance of the students

Zeithaml ( 1988 ) describes that satisfaction is the outcome result of the performance of any educational institute. According to Kotler and Clarke ( 1986 ), satisfaction is the desired outcome of any aim that amuses any individual’s admiration. Quality interactions between instructor and students lead to student satisfaction (Malik et al., 2010 ; Martínez-Argüelles et al., 2016 ). Teaching quality and course material enhances the student satisfaction by successful outcomes (Sanderson, 1995 ). Satisfaction relates to the student performance in terms of motivation, learning, assurance, and retention (Biner et al., 1996 ). Mensink and King ( 2020 ) described that performance is the conclusion of student-teacher efforts, and it shows the interest of students in the studies. The critical element in education is students’ academic performance (Rono, 2013 ). Therefore, it is considered as center pole, and the entire education system rotates around the student’s performance. Narad and Abdullah ( 2016 ) concluded that the students’ academic performance determines academic institutions’ success and failure.

Singh et al. ( 2016 ) asserted that the student academic performance directly influences the country’s socio-economic development. Farooq et al. ( 2011 ) highlights the students’ academic performance is the primary concern of all faculties. Additionally, the main foundation of knowledge gaining and improvement of skills is student’s academic performance. According to Narad and Abdullah ( 2016 ), regular evaluation or examinations is essential over a specific period of time in assessing students’ academic performance for better outcomes. Hence the hypothesis that satisfaction significantly affects the performance of the students was included in this study.

H5: Students’ satisfaction positively affects the performance of the students.

3.6 Satisfaction as mediator

Sibanda et al. ( 2015 ) applied the goal theory to examine the factors persuading students’ academic performance that enlightens students’ significance connected to their satisfaction and academic achievement. According to this theory, students perform well if they know about factors that impact on their performance. Regarding the above variables, institutional factors that influence student satisfaction through performance include course design and quality of the instructor (DeBourgh, 2003 ; Lado et al., 2003 ), prompt feedback, and expectation (Fredericksen et al., 2000 ). Hence the hypothesis that quality of the instructor, course design, prompts feedback, and student expectations significantly affect the students’ performance through satisfaction was included in this study.

H6: Quality of the instructor, course design, prompt feedback, and student’ expectations affect the students’ performance through satisfaction.

H6a: Students’ satisfaction mediates the relationship between quality of the instructor and student’s performance.

H6b: Students’ satisfaction mediates the relationship between course design and student’s performance.

H6c: Students’ satisfaction mediates the relationship between prompt feedback and student’s performance.

H6d: Students’ satisfaction mediates the relationship between student’ expectations and student’s performance.

4.1 Participants

In this cross-sectional study, the data were collected from 544 respondents who were studying the management (B.B.A or M.B.A) and hotel management courses. The purposive sampling technique was used to collect the data. Descriptive statistics shows that 48.35% of the respondents were either MBA or BBA and rests of the respondents were hotel management students. The percentages of male students were (71%) and female students were (29%). The percentage of male students is almost double in comparison to females. The ages of the students varied from 18 to 35. The dominant group was those aged from 18 to 22, and which was the under graduation student group and their ratio was (94%), and another set of students were from the post-graduation course, which was (6%) only.

4.2 Materials

The research instrument consists of two sections. The first section is related to demographical variables such as discipline, gender, age group, and education level (under-graduate or post-graduate). The second section measures the six factors viz. instructor’s quality, course design, prompt feedback, student expectations, satisfaction, and performance. These attributes were taken from previous studies (Yin & Wang, 2015 ; Bangert, 2004 ; Chickering & Gamson, 1987 ; Wilson et al., 1997 ). The “instructor quality” was measured through the scale developed by Bangert ( 2004 ). The scale consists of seven items. The “course design” and “prompt feedback” items were adapted from the research work of Bangert ( 2004 ). The “course design” scale consists of six items. The “prompt feedback” scale consists of five items. The “students’ expectation” scale consists of five items. Four items were adapted from Bangert, 2004 and one item was taken from Wilson et al. ( 1997 ). Students’ satisfaction was measure with six items taken from Bangert ( 2004 ); Wilson et al. ( 1997 ); Yin and Wang ( 2015 ). The “students’ performance” was measured through the scale developed by Wilson et al. ( 1997 ). The scale consists of six items. These variables were accessed on a five-point likert scale, ranging from 1(strongly disagree) to 5(strongly agree). Only the students from India have taken part in the survey. A total of thirty-four questions were asked in the study to check the effect of the first four variables on students’ satisfaction and performance. For full details of the questionnaire, kindly refer Appendix Tables 6 .

The study used a descriptive research design. The factors “instructor quality, course design, prompt feedback and students’ expectation” were independent variables. The students’ satisfaction was mediator and students’ performance was the dependent variable in the current study.

4.4 Procedure

In this cross-sectional research the respondents were selected through judgment sampling. They were informed about the objective of the study and information gathering process. They were assured about the confidentiality of the data and no incentive was given to then for participating in this study. The information utilizes for this study was gathered through an online survey. The questionnaire was built through Google forms, and then it was circulated through the mails. Students’ were also asked to write the name of their college, and fifteen colleges across India have taken part to fill the data. The data were collected in the pandemic period of COVID-19 during the total lockdown in India. This was the best time to collect the data related to the current research topic because all the colleges across India were involved in online classes. Therefore, students have enough time to understand the instrument and respondent to the questionnaire in an effective manner. A total of 615 questionnaires were circulated, out of which the students returned 574. Thirty responses were not included due to the unengaged responses. Finally, 544 questionnaires were utilized in the present investigation. Male and female students both have taken part to fill the survey, different age groups, and various courses, i.e., under graduation and post-graduation students of management and hotel management students were the part of the sample.

5.1 Exploratory factor analysis (EFA)

To analyze the data, SPSS and AMOS software were used. First, to extract the distinct factors, an exploratory factor analysis (EFA) was performed using VARIMAX rotation on a sample of 544. Results of the exploratory analysis rendered six distinct factors. Factor one was named as the quality of instructor, and some of the items were “The instructor communicated effectively”, “The instructor was enthusiastic about online teaching” and “The instructor was concerned about student learning” etc. Factor two was labeled as course design, and the items were “The course was well organized”, “The course was designed to allow assignments to be completed across different learning environments.” and “The instructor facilitated the course effectively” etc. Factor three was labeled as prompt feedback of students, and some of the items were “The instructor responded promptly to my questions about the use of Webinar”, “The instructor responded promptly to my questions about general course requirements” etc. The fourth factor was Student’s Expectations, and the items were “The instructor provided models that clearly communicated expectations for weekly group assignments”, “The instructor used good examples to explain statistical concepts” etc. The fifth factor was students’ satisfaction, and the items were “The online classes were valuable”, “Overall, I am satisfied with the quality of this course” etc. The sixth factor was performance of the student, and the items were “The online classes has sharpened my analytic skills”, “Online classes really tries to get the best out of all its students” etc. These six factors explained 67.784% of the total variance. To validate the factors extracted through EFA, the researcher performed confirmatory factor analysis (CFA) through AMOS. Finally, structural equation modeling (SEM) was used to test the hypothesized relationships.

5.2 Measurement model

The results of Table 1 summarize the findings of EFA and CFA. Results of the table showed that EFA renders six distinct factors, and CFA validated these factors. Table 2 shows that the proposed measurement model achieved good convergent validity (Aggarwal et al., 2018a , b ). Results of the confirmatory factor analysis showed that the values of standardized factor loadings were statistically significant at the 0.05 level. Further, the results of the measurement model also showed acceptable model fit indices such that CMIN = 710.709; df = 480; CMIN/df = 1.481 p  < .000; Incremental Fit Index (IFI) = 0.979; Tucker-Lewis Index (TLI) = 0.976; Goodness of Fit index (GFI) = 0.928; Adjusted Goodness of Fit Index (AGFI) = 0.916; Comparative Fit Index (CFI) = 0.978; Root Mean Square Residual (RMR) = 0.042; Root Mean Squared Error of Approximation (RMSEA) = 0.030 is satisfactory.

The Average Variance Explained (AVE) according to the acceptable index should be higher than the value of squared correlations between the latent variables and all other variables. The discriminant validity is confirmed (Table 2 ) as the value of AVE’s square root is greater than the inter-construct correlations coefficient (Hair et al., 2006 ). Additionally, the discriminant validity existed when there was a low correlation between each variable measurement indicator with all other variables except with the one with which it must be theoretically associated (Aggarwal et al., 2018a , b ; Aggarwal et al., 2020 ). The results of Table 2 show that the measurement model achieved good discriminate validity.

5.3 Structural model

To test the proposed hypothesis, the researcher used the structural equation modeling technique. This is a multivariate statistical analysis technique, and it includes the amalgamation of factor analysis and multiple regression analysis. It is used to analyze the structural relationship between measured variables and latent constructs.

Table  3 represents the structural model’s model fitness indices where all variables put together when CMIN/DF is 2.479, and all the model fit values are within the particular range. That means the model has attained a good model fit. Furthermore, other fit indices as GFI = .982 and AGFI = 0.956 be all so supportive (Schumacker & Lomax, 1996 ; Marsh & Grayson, 1995 ; Kline, 2005 ).

Hence, the model fitted the data successfully. All co-variances among the variables and regression weights were statistically significant ( p  < 0.001).

Table 4 represents the relationship between exogenous, mediator and endogenous variables viz—quality of instructor, prompt feedback, course design, students’ expectation, students’ satisfaction and students’ performance. The first four factors have a positive relationship with satisfaction, which further leads to students’ performance positively. Results show that the instructor’s quality has a positive relationship with the satisfaction of students for online classes (SE = 0.706, t-value = 24.196; p  < 0.05). Hence, H1 was supported. The second factor is course design, which has a positive relationship with students’ satisfaction of students (SE = 0.064, t-value = 2.395; p < 0.05). Hence, H2 was supported. The third factor is Prompt feedback, and results show that feedback has a positive relationship with the satisfaction of the students (SE = 0.067, t-value = 2.520; p < 0.05). Hence, H3 was supported. The fourth factor is students’ expectations. The results show a positive relationship between students’ expectation and students’ satisfaction with online classes (SE = 0.149, t-value = 5.127; p < 0.05). Hence, H4 was supported. The results of SEM show that out of quality of instructor, prompt feedback, course design, and students’ expectation, the most influencing factor that affect the students’ satisfaction was instructor’s quality (SE = 0.706) followed by students’ expectation (SE =5.127), prompt feedback (SE = 2.520). The factor that least affects the students’ satisfaction was course design (2.395). The results of Table 4 finally depicts that students’ satisfaction has positive effect on students’ performance ((SE = 0.186, t-value = 2.800; p < 0.05). Hence H5 was supported.

Table 5 shows that students’ satisfaction partially mediates the positive relationship between the instructor’s quality and student performance. Hence, H6(a) was supported. Further, the mediation analysis results showed that satisfaction again partially mediates the positive relationship between course design and student’s performance. Hence, H6(b) was supported However, the mediation analysis results showed that satisfaction fully mediates the positive relationship between prompt feedback and student performance. Hence, H6(c) was supported. Finally, the results of the Table 5 showed that satisfaction partially mediates the positive relationship between expectations of the students and student’s performance. Hence, H6(d) was supported.

6 Discussion

In the present study, the authors evaluated the different factors directly linked with students’ satisfaction and performance with online classes during Covid-19. Due to the pandemic situation globally, all the colleges and universities were shifted to online mode by their respective governments. No one has the information that how long this pandemic will remain, and hence the teaching method was shifted to online mode. Even though some of the educators were not tech-savvy, they updated themselves to battle the unexpected circumstance (Pillai et al., 2021 ). The present study results will help the educators increase the student’s satisfaction and performance in online classes. The current research assists educators in understanding the different factors that are required for online teaching.

Comparing the current research with past studies, the past studies have examined the factors affecting the student’s satisfaction in the conventional schooling framework. However, the present study was conducted during India’s lockdown period to identify the prominent factors that derive the student’s satisfaction with online classes. The study also explored the direct linkage between student’s satisfaction and their performance. The present study’s findings indicated that instructor’s quality is the most prominent factor that affects the student’s satisfaction during online classes. This means that the instructor needs to be very efficient during the lectures. He needs to understand students’ psychology to deliver the course content prominently. If the teacher can deliver the course content properly, it affects the student’s satisfaction and performance. The teachers’ perspective is critical because their enthusiasm leads to a better online learning process quality.

The present study highlighted that the second most prominent factor affecting students’ satisfaction during online classes is the student’s expectations. Students might have some expectations during the classes. If the instructor understands that expectation and customizes his/her course design following the student’s expectations, then it is expected that the students will perform better in the examinations. The third factor that affects the student’s satisfaction is feedback. After delivering the course, appropriate feedback should be taken by the instructors to plan future courses. It also helps to make the future strategies (Tawafak et al., 2019 ). There must be a proper feedback system for improvement because feedback is the course content’s real image. The last factor that affects the student’s satisfaction is design. The course content needs to be designed in an effective manner so that students should easily understand it. If the instructor plans the course, so the students understand the content without any problems it effectively leads to satisfaction, and the student can perform better in the exams. In some situations, the course content is difficult to deliver in online teaching like the practical part i.e. recipes of dishes or practical demonstration in the lab. In such a situation, the instructor needs to be more creative in designing and delivering the course content so that it positively impacts the students’ overall satisfaction with online classes.

Overall, the students agreed that online teaching was valuable for them even though the online mode of classes was the first experience during the pandemic period of Covid-19 (Agarwal & Kaushik, 2020 ; Rajabalee & Santally, 2020 ). Some of the previous studies suggest that the technology-supported courses have a positive relationship with students’ performance (Cho & Schelzer, 2000 ; Harasim, 2000 ; Sigala, 2002 ). On the other hand, the demographic characteristic also plays a vital role in understanding the online course performance. According to APA Work Group of the Board of Educational Affairs ( 1997 ), the learner-centered principles suggest that students must be willing to invest the time required to complete individual course assignments. Online instructors must be enthusiastic about developing genuine instructional resources that actively connect learners and encourage them toward proficient performances. For better performance in studies, both teachers and students have equal responsibility. When the learner faces any problem to understand the concepts, he needs to make inquiries for the instructor’s solutions (Bangert, 2004 ). Thus, we can conclude that “instructor quality, student’s expectation, prompt feedback, and effective course design” significantly impact students’ online learning process.

7 Implications of the study

The results of this study have numerous significant practical implications for educators, students and researchers. It also contributes to the literature by demonstrating that multiple factors are responsible for student satisfaction and performance in the context of online classes during the period of the COVID-19 pandemic. This study was different from the previous studies (Baber, 2020 ; Ikhsan et al., 2019 ; Eom & Ashill, 2016 ). None of the studies had examined the effect of students’ satisfaction on their perceived academic performance. The previous empirical findings have highlighted the importance of examining the factors affecting student satisfaction (Maqableh & Jaradat, 2021 ; Yunusa & Umar, 2021 ). Still, none of the studies has examined the effect of course design, quality of instructor, prompt feedback, and students’ expectations on students’ satisfaction all together with online classes during the pandemic period. The present study tries to fill this research gap.

The first essential contribution of this study was the instructor’s facilitating role, and the competence he/she possesses affects the level of satisfaction of the students (Gray & DiLoreto, 2016 ). There was an extra obligation for instructors who taught online courses during the pandemic. They would have to adapt to a changing climate, polish their technical skills throughout the process, and foster new students’ technical knowledge in this environment. The present study’s findings indicate that instructor quality is a significant determinant of student satisfaction during online classes amid a pandemic. In higher education, the teacher’s standard referred to the instructor’s specific individual characteristics before entering the class (Darling-Hammond, 2010 ). These attributes include factors such as instructor content knowledge, pedagogical knowledge, inclination, and experience. More significantly, at that level, the amount of understanding could be given by those who have a significant amount of technical expertise in the areas they are teaching (Martin, 2021 ). Secondly, the present study results contribute to the profession of education by illustrating a realistic approach that can be used to recognize students’ expectations in their class effectively. The primary expectation of most students before joining a university is employment. Instructors have agreed that they should do more to fulfill students’ employment expectations (Gorgodze et al., 2020 ). The instructor can then use that to balance expectations to improve student satisfaction. Study results can be used to continually improve and build courses, as well as to make policy decisions to improve education programs. Thirdly, from result outcomes, online course design and instructors will delve deeper into how to structure online courses more efficiently, including design features that minimize adversely and maximize optimistic emotion, contributing to greater student satisfaction (Martin et al., 2018 ). The findings suggest that the course design has a substantial positive influence on the online class’s student performance. The findings indicate that the course design of online classes need to provide essential details like course content, educational goals, course structure, and course output in a consistent manner so that students would find the e-learning system beneficial for them; this situation will enable students to use the system and that leads to student performance (Almaiah & Alyoussef, 2019 ). Lastly, the results indicate that instructors respond to questions promptly and provide timely feedback on assignments to facilitate techniques that help students in online courses improve instructor participation, instructor interaction, understanding, and participation (Martin et al., 2018 ). Feedback can be beneficial for students to focus on the performance that enhances their learning.

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Gopal, R., Singh, V. & Aggarwal, A. Impact of online classes on the satisfaction and performance of students during the pandemic period of COVID 19. Educ Inf Technol 26 , 6923–6947 (2021). https://doi.org/10.1007/s10639-021-10523-1

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Integrating students’ perspectives about online learning: a hierarchy of factors

• Montgomery Van Wart 1 ,
• Anna Ni 1 ,
• Pamela Medina 1 ,
• Jesus Canelon 1 ,
• Melika Kordrostami 1 ,
• Jing Zhang 1 &

International Journal of Educational Technology in Higher Education volume  17 , Article number:  53 ( 2020 ) Cite this article

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This article reports on a large-scale ( n  = 987), exploratory factor analysis study incorporating various concepts identified in the literature as critical success factors for online learning from the students’ perspective, and then determines their hierarchical significance. Seven factors--Basic Online Modality, Instructional Support, Teaching Presence, Cognitive Presence, Online Social Comfort, Online Interactive Modality, and Social Presence--were identified as significant and reliable. Regression analysis indicates the minimal factors for enrollment in future classes—when students consider convenience and scheduling—were Basic Online Modality, Cognitive Presence, and Online Social Comfort. Students who accepted or embraced online courses on their own merits wanted a minimum of Basic Online Modality, Teaching Presence, Cognitive Presence, Online Social Comfort, and Social Presence. Students, who preferred face-to-face classes and demanded a comparable experience, valued Online Interactive Modality and Instructional Support more highly. Recommendations for online course design, policy, and future research are provided.

Introduction

While there are different perspectives of the learning process such as learning achievement and faculty perspectives, students’ perspectives are especially critical since they are ultimately the raison d’être of the educational endeavor (Chickering & Gamson, 1987 ). More pragmatically, students’ perspectives provide invaluable, first-hand insights into their experiences and expectations (Dawson et al., 2019 ). The student perspective is especially important when new teaching approaches are used and when new technologies are being introduced (Arthur, 2009 ; Crews & Butterfield, 2014 ; Van Wart, Ni, Ready, Shayo, & Court, 2020 ). With the renewed interest in “active” education in general (Arruabarrena, Sánchez, Blanco, et al., 2019 ; Kay, MacDonald, & DiGiuseppe, 2019 ; Nouri, 2016 ; Vlachopoulos & Makri, 2017 ) and the flipped classroom approach in particular (Flores, del-Arco, & Silva, 2016 ; Gong, Yang, & Cai, 2020 ; Lundin, et al., 2018 ; Maycock, 2019 ; McGivney-Burelle, 2013 ; O’Flaherty & Phillips, 2015 ; Tucker , 2012 ) along with extraordinary shifts in the technology, the student perspective on online education is profoundly important. What shapes students’ perceptions of quality integrate are their own sense of learning achievement, satisfaction with the support they receive, technical proficiency of the process, intellectual and emotional stimulation, comfort with the process, and sense of learning community. The factors that students perceive as quality online teaching, however, has not been as clear as it might be for at least two reasons.

First, it is important to note that the overall online learning experience for students is also composed of non-teaching factors which we briefly mention. Three such factors are (1) convenience, (2) learner characteristics and readiness, and (3) antecedent conditions that may foster teaching quality but are not directly responsible for it. (1) Convenience is an enormous non-quality factor for students (Artino, 2010 ) which has driven up online demand around the world (Fidalgo, Thormann, Kulyk, et al., 2020 ; Inside Higher Education and Gallup, 2019 ; Legon & Garrett, 2019 ; Ortagus, 2017 ). This is important since satisfaction with online classes is frequently somewhat lower than face-to-face classes (Macon, 2011 ). However, the literature generally supports the relative equivalence of face-to-face and online modes regarding learning achievement criteria (Bernard et al., 2004 ; Nguyen, 2015 ; Ni, 2013 ; Sitzmann, Kraiger, Stewart, & Wisher, 2006 ; see Xu & Jaggars, 2014 for an alternate perspective). These contrasts are exemplified in a recent study of business students, in which online students using a flipped classroom approach outperformed their face-to-face peers, but ironically rated instructor performance lower (Harjoto, 2017 ). (2) Learner characteristics also affect the experience related to self-regulation in an active learning model, comfort with technology, and age, among others,which affect both receptiveness and readiness of online instruction. (Alqurashi, 2016 ; Cohen & Baruth, 2017 ; Kintu, Zhu, & Kagambe, 2017 ; Kuo, Walker, Schroder, & Belland, 2013 ; Ventura & Moscoloni, 2015 ) (3) Finally, numerous antecedent factors may lead to improved instruction, but are not themselves directly perceived by students such as instructor training (Brinkley-Etzkorn, 2018 ), and the sources of faculty motivation (e.g., incentives, recognition, social influence, and voluntariness) (Wingo, Ivankova, & Moss, 2017 ). Important as these factors are, mixing them with the perceptions of quality tends to obfuscate the quality factors directly perceived by students.

Second, while student perceptions of quality are used in innumerable studies, our overall understanding still needs to integrate them more holistically. Many studies use student perceptions of quality and overall effectiveness of individual tools and strategies in online contexts such as mobile devices (Drew & Mann, 2018 ), small groups (Choi, Land, & Turgeon, 2005 ), journals (Nair, Tay, & Koh, 2013 ), simulations (Vlachopoulos & Makri, 2017 ), video (Lange & Costley, 2020 ), etc. Such studies, however, cannot provide the overall context and comparative importance. Some studies have examined the overall learning experience of students with exploratory lists, but have mixed non-quality factors with quality of teaching factors making it difficult to discern the instructor’s versus contextual roles in quality (e.g., Asoodar, Vaezi, & Izanloo, 2016 ; Bollinger & Martindale, 2004 ; Farrell & Brunton, 2020 ; Hong, 2002 ; Song, Singleton, Hill, & Koh, 2004 ; Sun, Tsai, Finger, Chen, & Yeh, 2008 ). The application of technology adoption studies also fall into this category by essentially aggregating all teaching quality in the single category of performance ( Al-Gahtani, 2016 ; Artino, 2010 ). Some studies have used high-level teaching-oriented models, primarily the Community of Inquiry model (le Roux & Nagel, 2018 ), but empirical support has been mixed (Arbaugh et al., 2008 ); and its elegance (i.e., relying on only three factors) has not provided much insight to practitioners (Anderson, 2016 ; Cleveland-Innes & Campbell, 2012 ).

Research questions

Integration of studies and concepts explored continues to be fragmented and confusing despite the fact that the number of empirical studies related to student perceptions of quality factors has increased. It is important to have an empirical view of what students’ value in a single comprehensive study and, also, to know if there is a hierarchy of factors, ranging from students who are least to most critical of the online learning experience. This research study has two research questions.

The first research question is: What are the significant factors in creating a high-quality online learning experience from students’ perspectives? That is important to know because it should have a significant effect on the instructor’s design of online classes. The goal of this research question is identify a more articulated and empirically-supported set of factors capturing the full range of student expectations.

The second research question is: Is there a priority or hierarchy of factors related to students’ perceptions of online teaching quality that relate to their decisions to enroll in online classes? For example, is it possible to distinguish which factors are critical for enrollment decisions when students are primarily motivated by convenience and scheduling flexibility (minimum threshold)? Do these factors differ from students with a genuine acceptance of the general quality of online courses (a moderate threshold)? What are the factors that are important for the students who are the most critical of online course delivery (highest threshold)?

This article next reviews the literature on online education quality, focusing on the student perspective and reviews eight factors derived from it. The research methods section discusses the study structure and methods. Demographic data related to the sample are next, followed by the results, discussion, and conclusion.

Literature review

Online education is much discussed (Prinsloo, 2016 ; Van Wart et al., 2019 ; Zawacki-Richter & Naidu, 2016 ), but its perception is substantially influenced by where you stand and what you value (Otter et al., 2013 ; Tanner, Noser, & Totaro, 2009 ). Accrediting bodies care about meeting technical standards, proof of effectiveness, and consistency (Grandzol & Grandzol, 2006 ). Institutions care about reputation, rigor, student satisfaction, and institutional efficiency (Jung, 2011 ). Faculty care about subject coverage, student participation, faculty satisfaction, and faculty workload (Horvitz, Beach, Anderson, & Xia, 2015 ; Mansbach & Austin, 2018 ). For their part, students care about learning achievement (Marks, Sibley, & Arbaugh, 2005 ; O’Neill & Sai, 2014 ; Shen, Cho, Tsai, & Marra, 2013 ), but also view online education as a function of their enjoyment of classes, instructor capability and responsiveness, and comfort in the learning environment (e.g., Asoodar et al., 2016 ; Sebastianelli, Swift, & Tamimi, 2015 ). It is this last perspective, of students, upon which we focus.

It is important to note students do not sign up for online classes solely based on perceived quality. Perceptions of quality derive from notions of the capacity of online learning when ideal—relative to both learning achievement and satisfaction/enjoyment, and perceptions about the likelihood and experience of classes living up to expectations. Students also sign up because of convenience and flexibility, and personal notions of suitability about learning. Convenience and flexibility are enormous drivers of online registration (Lee, Stringer, & Du, 2017 ; Mann & Henneberry, 2012 ). Even when students say they prefer face-to-face classes to online, many enroll in online classes and re-enroll in the future if the experience meets minimum expectations. This study examines the threshold expectations of students when they are considering taking online classes.

When discussing students’ perceptions of quality, there is little clarity about the actual range of concepts because no integrated empirical studies exist comparing major factors found throughout the literature. Rather, there are practitioner-generated lists of micro-competencies such as the Quality Matters consortium for higher education (Quality Matters, 2018 ), or broad frameworks encompassing many aspects of quality beyond teaching (Open and Distant Learning Quality Council, 2012 ). While checklists are useful for practitioners and accreditation processes, they do not provide robust, theoretical bases for scholarly development. Overarching frameworks are heuristically useful, but not for pragmatic purposes or theory building arenas. The most prominent theoretical framework used in online literature is the Community of Inquiry (CoI) model (Arbaugh et al., 2008 ; Garrison, Anderson, & Archer, 2003 ), which divides instruction into teaching, cognitive, and social presence. Like deductive theories, however, the supportive evidence is mixed (Rourke & Kanuka, 2009 ), especially regarding the importance of social presence (Annand, 2011 ; Armellini and De Stefani, 2016 ). Conceptually, the problem is not so much with the narrow articulation of cognitive or social presence; cognitive presence is how the instructor provides opportunities for students to interact with material in robust, thought-provoking ways, and social presence refers to building a community of learning that incorporates student-to-student interactions. However, teaching presence includes everything else the instructor does—structuring the course, providing lectures, explaining assignments, creating rehearsal opportunities, supplying tests, grading, answering questions, and so on. These challenges become even more prominent in the online context. While the lecture as a single medium is paramount in face-to-face classes, it fades as the primary vehicle in online classes with increased use of detailed syllabi, electronic announcements, recorded and synchronous lectures, 24/7 communications related to student questions, etc. Amassing the pedagogical and technological elements related to teaching under a single concept provides little insight.

In addition to the CoI model, numerous concepts are suggested in single-factor empirical studies when focusing on quality from a student’s perspective, with overlapping conceptualizations and nonstandardized naming conventions. Seven distinct factors are derived here from the literature of student perceptions of online quality: Instructional Support, Teaching Presence, Basic Online Modality, Social Presence, Online Social Comfort, cognitive Presence, and Interactive Online Modality.

Instructional support

Instructional Support refers to students’ perceptions of techniques by the instructor used for input, rehearsal, feedback, and evaluation. Specifically, this entails providing detailed instructions, designed use of multimedia, and the balance between repetitive class features for ease of use, and techniques to prevent boredom. Instructional Support is often included as an element of Teaching Presence, but is also labeled “structure” (Lee & Rha, 2009 ; So & Brush, 2008 ) and instructor facilitation (Eom, Wen, & Ashill, 2006 ). A prime example of the difference between face-to-face and online education is the extensive use of the “flipped classroom” (Maycock, 2019 ; Wang, Huang, & Schunn, 2019 ) in which students move to rehearsal activities faster and more frequently than traditional classrooms, with less instructor lecture (Jung, 2011 ; Martin, Wang, & Sadaf, 2018 ). It has been consistently supported as an element of student perceptions of quality (Espasa & Meneses, 2010 ).

• Teaching presence

Teaching Presence refers to students’ perceptions about the quality of communication in lectures, directions, and individual feedback including encouragement (Jaggars & Xu, 2016 ; Marks et al., 2005 ). Specifically, instructor communication is clear, focused, and encouraging, and instructor feedback is customized and timely. If Instructional Support is what an instructor does before the course begins and in carrying out those plans, then Teaching Presence is what the instructor does while the class is conducted and in response to specific circumstances. For example, a course could be well designed but poorly delivered because the instructor is distracted; or a course could be poorly designed but an instructor might make up for the deficit by spending time and energy in elaborate communications and ad hoc teaching techniques. It is especially important in student satisfaction (Sebastianelli et al., 2015 ; Young, 2006 ) and also referred to as instructor presence (Asoodar et al., 2016 ), learner-instructor interaction (Marks et al., 2005 ), and staff support (Jung, 2011 ). As with Instructional Support, it has been consistently supported as an element of student perceptions of quality.

Basic online modality

Basic Online Modality refers to the competent use of basic online class tools—online grading, navigation methods, online grade book, and the announcements function. It is frequently clumped with instructional quality (Artino, 2010 ), service quality (Mohammadi, 2015 ), instructor expertise in e-teaching (Paechter, Maier, & Macher, 2010 ), and similar terms. As a narrowly defined concept, it is sometimes called technology (Asoodar et al., 2016 ; Bollinger & Martindale, 2004 ; Sun et al., 2008 ). The only empirical study that did not find Basic Online Modality significant, as technology, was Sun et al. ( 2008 ). Because Basic Online Modality is addressed with basic instructor training, some studies assert the importance of training (e.g., Asoodar et al., 2016 ).

Social presence

Social Presence refers to students’ perceptions of the quality of student-to-student interaction. Social Presence focuses on the quality of shared learning and collaboration among students, such as in threaded discussion responses (Garrison et al., 2003 ; Kehrwald, 2008 ). Much emphasized but challenged in the CoI literature (Rourke & Kanuka, 2009 ), it has mixed support in the online literature. While some studies found Social Presence or related concepts to be significant (e.g., Asoodar et al., 2016 ; Bollinger & Martindale, 2004 ; Eom et al., 2006 ; Richardson, Maeda, Lv, & Caskurlu, 2017 ), others found Social Presence insignificant (Joo, Lim, & Kim, 2011 ; So & Brush, 2008 ; Sun et al., 2008 ).

Online social comfort

Online Social Comfort refers to the instructor’s ability to provide an environment in which anxiety is low, and students feel comfortable interacting even when expressing opposing viewpoints. While numerous studies have examined anxiety (e.g., Liaw & Huang, 2013 ; Otter et al., 2013 ; Sun et al., 2008 ), only one found anxiety insignificant (Asoodar et al., 2016 ); many others have not examined the concept.

• Cognitive presence

Cognitive Presence refers to the engagement of students such that they perceive they are stimulated by the material and instructor to reflect deeply and critically, and seek to understand different perspectives (Garrison et al., 2003 ). The instructor provides instructional materials and facilitates an environment that piques interest, is reflective, and enhances inclusiveness of perspectives (Durabi, Arrastia, Nelson, Cornille, & Liang, 2011 ). Cognitive Presence includes enhancing the applicability of material for student’s potential or current careers. Cognitive Presence is supported as significant in many online studies (e.g., Artino, 2010 ; Asoodar et al., 2016 ; Joo et al., 2011 ; Marks et al., 2005 ; Sebastianelli et al., 2015 ; Sun et al., 2008 ). Further, while many instructors perceive that cognitive presence is diminished in online settings, neuroscientific studies indicate this need not be the case (Takamine, 2017 ). While numerous studies failed to examine Cognitive Presence, this review found no studies that lessened its significance for students.

Interactive online modality

Interactive Online Modality refers to the “high-end” usage of online functionality. That is, the instructor uses interactive online class tools—video lectures, videoconferencing, and small group discussions—well. It is often included in concepts such as instructional quality (Artino, 2010 ; Asoodar et al., 2016 ; Mohammadi, 2015 ; Otter et al., 2013 ; Paechter et al., 2010 ) or engagement (Clayton, Blumberg, & Anthony, 2018 ). While individual methods have been investigated (e.g. Durabi et al., 2011 ), high-end engagement methods have not.

Other independent variables affecting perceptions of quality include age, undergraduate versus graduate status, gender, ethnicity/race, discipline, educational motivation of students, and previous online experience. While age has been found to be small or insignificant, more notable effects have been reported at the level-of-study, with graduate students reporting higher “success” (Macon, 2011 ), and community college students having greater difficulty with online classes (Legon & Garrett, 2019 ; Xu & Jaggars, 2014 ). Ethnicity and race have also been small or insignificant. Some situational variations and student preferences can be captured by paying attention to disciplinary differences (Arbaugh, 2005 ; Macon, 2011 ). Motivation levels of students have been reported to be significant in completion and achievement, with better students doing as well across face-to-face and online modes, and weaker students having greater completion and achievement challenges (Clayton et al., 2018 ; Lu & Lemonde, 2013 ).

Research methods

To examine the various quality factors, we apply a critical success factor methodology, initially introduced to schools of business research in the 1970s. In 1981, Rockhart and Bullen codified an approach embodying principles of critical success factors (CSFs) as a way to identify the information needs of executives, detailing steps for the collection and analyzation of data to create a set of organizational CSFs (Rockhart & Bullen, 1981 ). CSFs describe the underlying or guiding principles which must be incorporated to ensure success.

Utilizing this methodology, CSFs in the context of this paper define key areas of instruction and design essential for an online class to be successful from a student’s perspective. Instructors implicitly know and consider these areas when setting up an online class and designing and directing activities and tasks important to achieving learning goals. CSFs make explicit those things good instructors may intuitively know and (should) do to enhance student learning. When made explicit, CSFs not only confirm the knowledge of successful instructors, but tap their intuition to guide and direct the accomplishment of quality instruction for entire programs. In addition, CSFs are linked with goals and objectives, helping generate a small number of truly important matters an instructor should focus attention on to achieve different thresholds of online success.

After a comprehensive literature review, an instrument was created to measure students’ perceptions about the importance of techniques and indicators leading to quality online classes. Items were designed to capture the major factors in the literature. The instrument was pilot studied during academic year 2017–18 with a 397 student sample, facilitating an exploratory factor analysis leading to important preliminary findings (reference withheld for review). Based on the pilot, survey items were added and refined to include seven groups of quality teaching factors and two groups of items related to students’ overall acceptance of online classes as well as a variable on their future online class enrollment. Demographic information was gathered to determine their effects on students’ levels of acceptance of online classes based on age, year in program, major, distance from university, number of online classes taken, high school experience with online classes, and communication preferences.

This paper draws evidence from a sample of students enrolled in educational programs at Jack H. Brown College of Business and Public Administration (JHBC), California State University San Bernardino (CSUSB). The JHBC offers a wide range of online courses for undergraduate and graduate programs. To ensure comparable learning outcomes, online classes and face-to-face classes of a certain subject are similar in size—undergraduate classes are generally capped at 60 and graduate classes at 30, and often taught by the same instructors. Students sometimes have the option to choose between both face-to-face and online modes of learning.

A Qualtrics survey link was sent out by 11 instructors to students who were unlikely to be cross-enrolled in classes during the 2018–19 academic year. 1 Approximately 2500 students were contacted, with some instructors providing class time to complete the anonymous survey. All students, whether they had taken an online class or not, were encouraged to respond. Nine hundred eighty-seven students responded, representing a 40% response rate. Although drawn from a single business school, it is a broad sample representing students from several disciplines—management, accounting and finance, marketing, information decision sciences, and public administration, as well as both graduate and undergraduate programs of study.

The sample age of students is young, with 78% being under 30. The sample has almost no lower division students (i.e., freshman and sophomore), 73% upper division students (i.e., junior and senior) and 24% graduate students (master’s level). Only 17% reported having taken a hybrid or online class in high school. There was a wide range of exposure to university level online courses, with 47% reporting having taken 1 to 4 classes, and 21% reporting no online class experience. As a Hispanic-serving institution, 54% self-identified as Latino, 18% White, and 13% Asian and Pacific Islander. The five largest majors were accounting & finance (25%), management (21%), master of public administration (16%), marketing (12%), and information decision sciences (10%). Seventy-four percent work full- or part-time. See Table  1 for demographic data.

Measures and procedure

To increase the reliability of evaluation scores, composite evaluation variables are formed after an exploratory factor analysis of individual evaluation items. A principle component method with Quartimin (oblique) rotation was applied to explore the factor construct of student perceptions of online teaching CSFs. The item correlations for student perceptions of importance coefficients greater than .30 were included, a commonly acceptable ratio in factor analysis. A simple least-squares regression analysis was applied to test the significance levels of factors on students’ impression of online classes.

Exploratory factor constructs

Using a threshold loading of 0.3 for items, 37 items loaded on seven factors. All factors were logically consistent. The first factor, with eight items, was labeled Teaching Presence. Items included providing clear instructions, staying on task, clear deadlines, and customized feedback on strengths and weaknesses. Teaching Presence items all related to instructor involvement during the course as a director, monitor, and learning facilitator. The second factor, with seven items, aligned with Cognitive Presence. Items included stimulating curiosity, opportunities for reflection, helping students construct explanations posed in online courses, and the applicability of material. The third factor, with six items, aligned with Social Presence defined as providing student-to-student learning opportunities. Items included getting to know course participants for sense of belonging, forming impressions of other students, and interacting with others. The fourth factor, with six new items as well as two (“interaction with other students” and “a sense of community in the class”) shared with the third factor, was Instructional Support which related to the instructor’s roles in providing students a cohesive learning experience. They included providing sufficient rehearsal, structured feedback, techniques for communication, navigation guide, detailed syllabus, and coordinating student interaction and creating a sense of online community. This factor also included enthusiasm which students generally interpreted as a robustly designed course, rather than animation in a traditional lecture. The fifth factor was labeled Basic Online Modality and focused on the basic technological requirements for a functional online course. Three items included allowing students to make online submissions, use of online gradebooks, and online grading. A fourth item is the use of online quizzes, viewed by students as mechanical practice opportunities rather than small tests and a fifth is navigation, a key component of Online Modality. The sixth factor, loaded on four items, was labeled Online Social Comfort. Items here included comfort discussing ideas online, comfort disagreeing, developing a sense of collaboration via discussion, and considering online communication as an excellent medium for social interaction. The final factor was called Interactive Online Modality because it included items for “richer” communications or interactions, no matter whether one- or two-way. Items included videoconferencing, instructor-generated videos, and small group discussions. Taken together, these seven explained 67% of the variance which is considered in the acceptable range in social science research for a robust model (Hair, Black, Babin, & Anderson, 2014 ). See Table  2 for the full list.

To test for factor reliability, the Cronbach alpha of variables were calculated. All produced values greater than 0.7, the standard threshold used for reliability, except for system trust which was therefore dropped. To gauge students’ sense of factor importance, all items were means averaged. Factor means (lower means indicating higher importance to students), ranged from 1.5 to 2.6 on a 5-point scale. Basic Online Modality was most important, followed by Instructional Support and Teaching Presence. Students deemed Cognitive Presence, Social Online Comfort, and Online Interactive Modality less important. The least important for this sample was Social Presence. Table  3 arrays the critical success factor means, standard deviations, and Cronbach alpha.

To determine whether particular subgroups of respondents viewed factors differently, a series of ANOVAs were conducted using factor means as dependent variables. Six demographic variables were used as independent variables: graduate vs. undergraduate, age, work status, ethnicity, discipline, and past online experience. To determine strength of association of the independent variables to each of the seven CSFs, eta squared was calculated for each ANOVA. Eta squared indicates the proportion of variance in the dependent variable explained by the independent variable. Eta squared values greater than .01, .06, and .14 are conventionally interpreted as small, medium, and large effect sizes, respectively (Green & Salkind, 2003 ). Table  4 summarizes the eta squared values for the ANOVA tests with Eta squared values less than .01 omitted.

While no significant differences in factor means among students in different disciplines in the College occur, all five other independent variables have some small effect on some or all CSFs. Graduate students tend to rate Online Interactive Modality, Instructional Support, Teaching Presence, and Cognitive Presence higher than undergraduates. Elder students value more Online Interactive Modality. Full-time working students rate all factors, except Social Online Comfort, slightly higher than part-timers and non-working students. Latino and White rate Basic Online Modality and Instructional Support higher; Asian and Pacific Islanders rate Social Presence higher. Students who have taken more online classes rate all factors higher.

In addition to factor scores, two variables are constructed to identify the resultant impressions labeled online experience. Both were logically consistent with a Cronbach’s α greater than 0.75. The first variable, with six items, labeled “online acceptance,” included items such as “I enjoy online learning,” “My overall impression of hybrid/online learning is very good,” and “the instructors of online/hybrid classes are generally responsive.” The second variable was labeled “face-to-face preference” and combines four items, including enjoying, learning, and communicating more in face-to-face classes, as well as perceiving greater fairness and equity. In addition to these two constructed variables, a one-item variable was also used subsequently in the regression analysis: “online enrollment.” That question asked: if hybrid/online classes are well taught and available, how much would online education make up your entire course selection going forward?

Regression results

As noted above, two constructed variables and one item were used as dependent variables for purposes of regression analysis. They were online acceptance, F2F preference, and the selection of online classes. In addition to seven quality-of-teaching factors identified by factor analysis, control variables included level of education (graduate versus undergraduate), age, ethnicity, work status, distance to university, and number of online/hybrid classes taken in the past. See Table  5 .

When the ETA squared values for ANOVA significance were measured for control factors, only one was close to a medium effect. Graduate versus undergraduate status had a .05 effect (considered medium) related to Online Interactive Modality, meaning graduate students were more sensitive to interactive modality than undergraduates. Multiple regression analysis of critical success factors and online impressions were conducted to compare under what conditions factors were significant. The only consistently significant control factor was number of online classes taken. The more classes students had taken online, the more inclined they were to take future classes. Level of program, age, ethnicity, and working status do not significantly affect students’ choice or overall acceptance of online classes.

The least restrictive condition was online enrollment (Table  6 ). That is, students might not feel online courses were ideal, but because of convenience and scheduling might enroll in them if minimum threshold expectations were met. When considering online enrollment three factors were significant and positive (at the 0.1 level): Basic Online Modality, Cognitive Presence, and Online Social Comfort. These least-demanding students expected classes to have basic technological functionality, provide good opportunities for knowledge acquisition, and provide comfortable interaction in small groups. Students who demand good Instructional Support (e.g., rehearsal opportunities, standardized feedback, clear syllabus) are less likely to enroll.

Online acceptance was more restrictive (see Table  7 ). This variable captured the idea that students not only enrolled in online classes out of necessity, but with an appreciation of the positive attributes of online instruction, which balanced the negative aspects. When this standard was applied, students expected not only Basic Online Modality, Cognitive Presence, and Online Social Comfort, but expected their instructors to be highly engaged virtually as the course progressed (Teaching Presence), and to create strong student-to-student dynamics (Social Presence). Students who rated Instructional Support higher are less accepting of online classes.

Another restrictive condition was catering to the needs of students who preferred face-to-face classes (see Table  8 ). That is, they preferred face-to-face classes even when online classes were well taught. Unlike students more accepting of, or more likely to enroll in, online classes, this group rates Instructional Support as critical to enrolling, rather than a negative factor when absent. Again different from the other two groups, these students demand appropriate interactive mechanisms (Online Interactive Modality) to enable richer communication (e.g., videoconferencing). Student-to-student collaboration (Social Presence) was also significant. This group also rated Cognitive Presence and Online Social Comfort as significant, but only in their absence. That is, these students were most attached to direct interaction with the instructor and other students rather than specific teaching methods. Interestingly, Basic Online Modality and Teaching Presence were not significant. Our interpretation here is this student group, most critical of online classes for its loss of physical interaction, are beyond being concerned with mechanical technical interaction and demand higher levels of interactivity and instructional sophistication.

Discussion and study limitations

Some past studies have used robust empirical methods to identify a single factor or a small number of factors related to quality from a student’s perspective, but have not sought to be relatively comprehensive. Others have used a longer series of itemized factors, but have less used less robust methods, and have not tied those factors back to the literature. This study has used the literature to develop a relatively comprehensive list of items focused on quality teaching in a single rigorous protocol. That is, while a Beta test had identified five coherent factors, substantial changes to the current survey that sharpened the focus on quality factors rather than antecedent factors, as well as better articulating the array of factors often lumped under the mantle of “teaching presence.” In addition, it has also examined them based on threshold expectations: from minimal, such as when flexibility is the driving consideration, to modest, such as when students want a “good” online class, to high, when students demand an interactive virtual experience equivalent to face-to-face.

Exploratory factor analysis identified seven factors that were reliable, coherent, and significant under different conditions. When considering students’ overall sense of importance, they are, in order: Basic Online Modality, Instructional Support, Teaching Presence, Cognitive Presence, Social Online Comfort, Interactive Online Modality, and Social Presence. Students are most concerned with the basics of a course first, that is the technological and instructor competence. Next they want engagement and virtual comfort. Social Presence, while valued, is the least critical from this overall perspective.

The factor analysis is quite consistent with the range of factors identified in the literature, pointing to the fact that students can differentiate among different aspects of what have been clumped as larger concepts, such as teaching presence. Essentially, the instructor’s role in quality can be divided into her/his command of basic online functionality, good design, and good presence during the class. The instructor’s command of basic functionality is paramount. Because so much of online classes must be built in advance of the class, quality of the class design is rated more highly than the instructor’s role in facilitating the class. Taken as a whole, the instructor’s role in traditional teaching elements is primary, as we would expect it to be. Cognitive presence, especially as pertinence of the instructional material and its applicability to student interests, has always been found significant when studied, and was highly rated as well in a single factor. Finally, the degree to which students feel comfortable with the online environment and enjoy the learner-learner aspect has been less supported in empirical studies, was found significant here, but rated the lowest among the factors of quality to students.

Regression analysis paints a more nuanced picture, depending on student focus. It also helps explain some of the heterogeneity of previous studies, depending on what the dependent variables were. If convenience and scheduling are critical and students are less demanding, minimum requirements are Basic Online Modality, Cognitive Presence, and Online Social Comfort. That is, students’ expect an instructor who knows how to use an online platform, delivers useful information, and who provides a comfortable learning environment. However, they do not expect to get poor design. They do not expect much in terms of the quality teaching presence, learner-to-learner interaction, or interactive teaching.

When students are signing up for critical classes, or they have both F2F and online options, they have a higher standard. That is, they not only expect the factors for decisions about enrolling in noncritical classes, but they also expect good Teaching and Social Presence. Students who simply need a class may be willing to teach themselves a bit more, but students who want a good class expect a highly present instructor in terms responsiveness and immediacy. “Good” classes must not only create a comfortable atmosphere, but in social science classes at least, must provide strong learner-to-learner interactions as well. At the time of the research, most students believe that you can have a good class without high interactivity via pre-recorded video and videoconference. That may, or may not, change over time as technology thresholds of various video media become easier to use, more reliable, and more commonplace.

The most demanding students are those who prefer F2F classes because of learning style preferences, poor past experiences, or both. Such students (seem to) assume that a worthwhile online class has basic functionality and that the instructor provides a strong presence. They are also critical of the absence of Cognitive Presence and Online Social Comfort. They want strong Instructional Support and Social Presence. But in addition, and uniquely, they expect Online Interactive Modality which provides the greatest verisimilitude to the traditional classroom as possible. More than the other two groups, these students crave human interaction in the learning process, both with the instructor and other students.

These findings shed light on the possible ramifications of the COVID-19 aftermath. Many universities around the world jumped from relatively low levels of online instruction in the beginning of spring 2020 to nearly 100% by mandate by the end of the spring term. The question becomes, what will happen after the mandate is removed? Will demand resume pre-crisis levels, will it increase modestly, or will it skyrocket? Time will be the best judge, but the findings here would suggest that the ability/interest of instructors and institutions to “rise to the occasion” with quality teaching will have as much effect on demand as students becoming more acclimated to online learning. If in the rush to get classes online many students experience shoddy basic functional competence, poor instructional design, sporadic teaching presence, and poorly implemented cognitive and social aspects, they may be quite willing to return to the traditional classroom. If faculty and institutions supporting them are able to increase the quality of classes despite time pressures, then most students may be interested in more hybrid and fully online classes. If instructors are able to introduce high quality interactive teaching, nearly the entire student population will be interested in more online classes. Of course students will have a variety of experiences, but this analysis suggests that those instructors, departments, and institutions that put greater effort into the temporary adjustment (and who resist less), will be substantially more likely to have increases in demand beyond what the modest national trajectory has been for the last decade or so.

There are several study limitations. First, the study does not include a sample of non-respondents. Non-responders may have a somewhat different profile. Second, the study draws from a single college and university. The profile derived here may vary significantly by type of student. Third, some survey statements may have led respondents to rate quality based upon experience rather than assess the general importance of online course elements. “I felt comfortable participating in the course discussions,” could be revised to “comfort in participating in course discussions.” The authors weighed differences among subgroups (e.g., among majors) as small and statistically insignificant. However, it is possible differences between biology and marketing students would be significant, leading factors to be differently ordered. Emphasis and ordering might vary at a community college versus research-oriented university (Gonzalez, 2009 ).

Availability of data and materials

We will make the data available.

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Van Wart, M., Ni, A., Medina, P. et al. Integrating students’ perspectives about online learning: a hierarchy of factors. Int J Educ Technol High Educ 17 , 53 (2020). https://doi.org/10.1186/s41239-020-00229-8

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• Entertainment

Online learning worse for concentration, eyes, but better for info access: Japan research

June 8, 2024 (Mainichi Japan)

Japanese version

TOYAMA -- Compared to pen and paper, studying via digital devices makes it harder to concentrate and strains the eyes, but has its benefits as well, according to research results by a Japanese university team published last month.

The team from the University of Toyama reviewed the advantages and disadvantages of digital-based learning, which took off amid the coronavirus pandemic. At their university, online lectures were introduced in 2020 and paper printouts were ended. Even after in-person classes resumed, the number of students requiring printouts fell sharply.

The Department of Epidemiology and Health Policy's assistant professor Masaaki Yamada, who had been studying the dangers of internet addiction, and others in April 2022 distributed a questionnaire to 939 undergraduate students in the schools of medicine, nursing and pharmaceutical science. The students were asked about their ease of understanding, memory, concentration and eyestrain regarding both online and paper-based learning, and answers were received from 344 of them.

No difference was found regarding ease of understanding, but paper was considered the better medium in the other three aspects. About 75% of respondents selected "paper is better" while around 6% chose "digital is better" regarding both concentration and memory. Roughly 85% selected "digital is worse" when it comes to eyestrain.

According to Yamada, a number of studies comparing paper-based and digital learning have been published in Japan and internationally, all of which have reported large merits for the traditional format. However, this study also found positives for digital studies, such as for ease of access to information and the ability to learn through audio, three-dimensional structure and other things.

Yamada pointed out that based on the results, paper-based can be considered better than digital learning in terms of concentration and memory retention. However, since a large number of students reported eyestrain associated with online learning, he commented, "This is an important health finding. Severe nearsightedness can lead to glaucoma and other diseases that cause blindness in the future, so it is necessary to reduce eyestrain by not continuously looking at digital devices."

Concerning the advancement of information and communications technology-based learning at elementary and junior high schools across the country, Yamada added, "Learning methods which consider the merits and demerits of both paper(-based) and digital learning are desirable."

The results were published on May 29 in the British online peer-reviewed journal BMJ Open. The full paper can be read at https://bmjopen.bmj.com/content/14/5/e083344 .

(Japanese original by Ikuko Aoyama, Toyama Bureau)

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Study shows online professional education works for complex topics

by Katherine Egan Bennett, University of Texas at Arlington

Online education is effective for teaching complicated topics like quantum information science (QIS) to high school science educators, according to a new paper by University of Texas at Arlington researchers published in The Physics Teacher .

"COVID-19 forced educators to adjust their educational best practices to an unfamiliar virtual classroom, and professional development was no different," said Karen Jo Matsler, assistant professor in practice for UTeach at UTA and lead author on the study.

Ramon Lopez, professor of physics, was coprincipal investigator on the project. Chandralekha Singh from the University of Pittsburgh was a co-author.

QIS is a new field of science and technology that combines physical science, math, computer science and engineering, and it is key to everyday items like cellphones and solar technology. However, most high schools don't teach the subject , preventing students from acquiring the skills they need to pursue lucrative jobs.

As part of a $1 million grant from the National Science Foundation in 2021, Matsler and her colleagues aimed to teach QIS to high school science teachers, who could then bring this newly acquired knowledge to their classrooms.

"However, the pandemic made us scrap our original plans for in-person training to an online environment," Matsler said. "We knew that teaching QIS online would be challenging, but we were pleasantly surprised how well it worked."

Matsler, Lopez and the team found that what worked best for teaching QIS online was sending participants some of the material in advance to allow them to become familiar with the topics. Then during the sessions, the educators used Zoom—with features such as chat, polling and breakout rooms—to keep the individuals engaged in learning. They also led activities where the learners had a chance to practice teaching the material, another technique that helped individuals stay engaged.

To avoid cognitive overload, the team found main discussions needed to be kept at 15 to 30 minutes, each with breakout sessions lasting five to seven minutes, with a total session time of about 90 to 120 minutes.

"This gave participants ample opportunities to discuss the quantum concepts in small groups varying from two to six participants," Matsler said. "During these small discussions, leaders rotated in and out of the rooms to check on the participants, clarify instructions and answer questions."

The instructors also recommend "icebreaker" activities to increase community engagement in virtual learning.

"These icebreaker activities can easily be used to engage students, take attendance and gauge how much the individuals know about the upcoming subject lesson," Matsler said. "A key element to all of this online learning is making sure the learners feel they are in a safe community to learn and exchange ideas."

The team also found that short, relevant videos helped teach complicated topics. They recommend keeping the chat function operational during videos to allow participants to ask questions and stay engaged.

"Ideally, QIS is taught in a classroom with hands-on activities to allow learners to see and touch how things like maglev trains and quantum levitation work," Matsler said.

"However, our experiences show that embedding appropriate pedagogy and content with online learning can be effective at teaching these topics. Understanding there is an effective virtual option is important as the country ramps up its efforts to accelerate quantum research and development to stay competitive with other countries in this field."

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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:

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Title: gpt-4 technical report.

Abstract: We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behavior. A core component of this project was developing infrastructure and optimization methods that behave predictably across a wide range of scales. This allowed us to accurately predict some aspects of GPT-4's performance based on models trained with no more than 1/1,000th the compute of GPT-4.

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