research paper on online teaching and learning

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Published: 25 January 2021

Online education in the post-COVID era

Barbara B. Lockee 1

Nature Electronics volume 4 , pages 5–6 ( 2021 ) Cite this article

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The coronavirus pandemic has forced students and educators across all levels of education to rapidly adapt to online learning. The impact of this — and the developments required to make it work — could permanently change how education is delivered.

The COVID-19 pandemic has forced the world to engage in the ubiquitous use of virtual learning. And while online and distance learning has been used before to maintain continuity in education, such as in the aftermath of earthquakes 1 , the scale of the current crisis is unprecedented. Speculation has now also begun about what the lasting effects of this will be and what education may look like in the post-COVID era. For some, an immediate retreat to the traditions of the physical classroom is required. But for others, the forced shift to online education is a moment of change and a time to reimagine how education could be delivered 2 .

research paper on online teaching and learning

Looking back

Online education has traditionally been viewed as an alternative pathway, one that is particularly well suited to adult learners seeking higher education opportunities. However, the emergence of the COVID-19 pandemic has required educators and students across all levels of education to adapt quickly to virtual courses. (The term ‘emergency remote teaching’ was coined in the early stages of the pandemic to describe the temporary nature of this transition 3 .) In some cases, instruction shifted online, then returned to the physical classroom, and then shifted back online due to further surges in the rate of infection. In other cases, instruction was offered using a combination of remote delivery and face-to-face: that is, students can attend online or in person (referred to as the HyFlex model 4 ). In either case, instructors just had to figure out how to make it work, considering the affordances and constraints of the specific learning environment to create learning experiences that were feasible and effective.

The use of varied delivery modes does, in fact, have a long history in education. Mechanical (and then later electronic) teaching machines have provided individualized learning programmes since the 1950s and the work of B. F. Skinner 5 , who proposed using technology to walk individual learners through carefully designed sequences of instruction with immediate feedback indicating the accuracy of their response. Skinner’s notions formed the first formalized representations of programmed learning, or ‘designed’ learning experiences. Then, in the 1960s, Fred Keller developed a personalized system of instruction 6 , in which students first read assigned course materials on their own, followed by one-on-one assessment sessions with a tutor, gaining permission to move ahead only after demonstrating mastery of the instructional material. Occasional class meetings were held to discuss concepts, answer questions and provide opportunities for social interaction. A personalized system of instruction was designed on the premise that initial engagement with content could be done independently, then discussed and applied in the social context of a classroom.

These predecessors to contemporary online education leveraged key principles of instructional design — the systematic process of applying psychological principles of human learning to the creation of effective instructional solutions — to consider which methods (and their corresponding learning environments) would effectively engage students to attain the targeted learning outcomes. In other words, they considered what choices about the planning and implementation of the learning experience can lead to student success. Such early educational innovations laid the groundwork for contemporary virtual learning, which itself incorporates a variety of instructional approaches and combinations of delivery modes.

Online learning and the pandemic

Fast forward to 2020, and various further educational innovations have occurred to make the universal adoption of remote learning a possibility. One key challenge is access. Here, extensive problems remain, including the lack of Internet connectivity in some locations, especially rural ones, and the competing needs among family members for the use of home technology. However, creative solutions have emerged to provide students and families with the facilities and resources needed to engage in and successfully complete coursework 7 . For example, school buses have been used to provide mobile hotspots, and class packets have been sent by mail and instructional presentations aired on local public broadcasting stations. The year 2020 has also seen increased availability and adoption of electronic resources and activities that can now be integrated into online learning experiences. Synchronous online conferencing systems, such as Zoom and Google Meet, have allowed experts from anywhere in the world to join online classrooms 8 and have allowed presentations to be recorded for individual learners to watch at a time most convenient for them. Furthermore, the importance of hands-on, experiential learning has led to innovations such as virtual field trips and virtual labs 9 . A capacity to serve learners of all ages has thus now been effectively established, and the next generation of online education can move from an enterprise that largely serves adult learners and higher education to one that increasingly serves younger learners, in primary and secondary education and from ages 5 to 18.

The COVID-19 pandemic is also likely to have a lasting effect on lesson design. The constraints of the pandemic provided an opportunity for educators to consider new strategies to teach targeted concepts. Though rethinking of instructional approaches was forced and hurried, the experience has served as a rare chance to reconsider strategies that best facilitate learning within the affordances and constraints of the online context. In particular, greater variance in teaching and learning activities will continue to question the importance of ‘seat time’ as the standard on which educational credits are based 10 — lengthy Zoom sessions are seldom instructionally necessary and are not aligned with the psychological principles of how humans learn. Interaction is important for learning but forced interactions among students for the sake of interaction is neither motivating nor beneficial.

While the blurring of the lines between traditional and distance education has been noted for several decades 11 , the pandemic has quickly advanced the erasure of these boundaries. Less single mode, more multi-mode (and thus more educator choices) is becoming the norm due to enhanced infrastructure and developed skill sets that allow people to move across different delivery systems 12 . The well-established best practices of hybrid or blended teaching and learning 13 have served as a guide for new combinations of instructional delivery that have developed in response to the shift to virtual learning. The use of multiple delivery modes is likely to remain, and will be a feature employed with learners of all ages 14 , 15 . Future iterations of online education will no longer be bound to the traditions of single teaching modes, as educators can support pedagogical approaches from a menu of instructional delivery options, a mix that has been supported by previous generations of online educators 16 .

Also significant are the changes to how learning outcomes are determined in online settings. Many educators have altered the ways in which student achievement is measured, eliminating assignments and changing assessment strategies altogether 17 . Such alterations include determining learning through strategies that leverage the online delivery mode, such as interactive discussions, student-led teaching and the use of games to increase motivation and attention. Specific changes that are likely to continue include flexible or extended deadlines for assignment completion 18 , more student choice regarding measures of learning, and more authentic experiences that involve the meaningful application of newly learned skills and knowledge 19 , for example, team-based projects that involve multiple creative and social media tools in support of collaborative problem solving.

In response to the COVID-19 pandemic, technological and administrative systems for implementing online learning, and the infrastructure that supports its access and delivery, had to adapt quickly. While access remains a significant issue for many, extensive resources have been allocated and processes developed to connect learners with course activities and materials, to facilitate communication between instructors and students, and to manage the administration of online learning. Paths for greater access and opportunities to online education have now been forged, and there is a clear route for the next generation of adopters of online education.

Before the pandemic, the primary purpose of distance and online education was providing access to instruction for those otherwise unable to participate in a traditional, place-based academic programme. As its purpose has shifted to supporting continuity of instruction, its audience, as well as the wider learning ecosystem, has changed. It will be interesting to see which aspects of emergency remote teaching remain in the next generation of education, when the threat of COVID-19 is no longer a factor. But online education will undoubtedly find new audiences. And the flexibility and learning possibilities that have emerged from necessity are likely to shift the expectations of students and educators, diminishing further the line between classroom-based instruction and virtual learning.

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

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1 Educational Leadership, University of North Carolina Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, USA.
PMID: 32921895
PMCID: PMC7480742
DOI: 10.1016/j.compedu.2020.104009

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.

Keywords: Distance education; Online Learning Research; Research Themes; Systematic Review; online teaching and learning.

A Survey on the Effectiveness of Online Teaching–Learning Methods for University and College Students

Article of professional interests
Published: 05 April 2021
Volume 102 , pages 1325–1334, ( 2021 )

Cite this article

Preethi Sheba Hepsiba Darius ORCID: orcid.org/0000-0003-0882-6213 1 ,
Edison Gundabattini ORCID: orcid.org/0000-0003-4217-2321 2 &
Darius Gnanaraj Solomon ORCID: orcid.org/0000-0001-5321-5775 2

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Online teaching–learning methods have been followed by world-class universities for more than a decade to cater to the needs of students who stay far away from universities/colleges. But during the COVID-19 pandemic period, online teaching–learning helped almost all universities, colleges, and affiliated students. An attempt is made to find the effectiveness of online teaching–learning methods for university and college students by conducting an online survey. A questionnaire has been specially designed and deployed among university and college students. About 450 students from various universities, engineering colleges, medical colleges in South India have taken part in the survey and submitted responses. It was found that the following methods promote effective online learning: animations, digital collaborations with peers, video lectures delivered by faculty handling the subject, online quiz having multiple-choice questions, availability of student version software, a conducive environment at home, interactions by the faculty during lectures and online materials provided by the faculty. Moreover, online classes are more effective because they provide PPTs in front of every student, lectures are heard by all students at the sound level of their choice, and walking/travel to reach classes is eliminated.

Avoid common mistakes on your manuscript.

Introduction

Critical thinking and creativity of students increase with innovative educational methods according to the world declaration on higher education in the twenty-first century [ 1 ]. Innovative educational strategies and educational innovations are required to make the students learn. There are three vertices in the teaching–learning process viz., teaching, communication technology through digital tools, and innovative practices in teaching. In the first vertex, the teacher is a facilitator and provides resources and tools to students and helps them to develop new knowledge and skills. Project-based learning helps teachers and students to promote collaborative learning by discussing specific topics. Cognitive independence is developed among students. To promote global learning, teachers are required to innovate permanently. It is possible when university professors and researchers are given space to new educational forms in different areas of specializations. Virtual classrooms, unlike traditional classrooms, give unlimited scope for introducing teaching innovation strategies. The second vertex refers to the use of Information and Communication Technology (ICT) tools for promoting innovative education. Learning management systems (LMS) help in teaching, learning, educational administration, testing, and evaluation. The use of ICT tools promotes technological innovations and advances in learning and knowledge management. The third vertex deals with innovations in teaching/learning to solve problems faced by teachers and students. Creative use of new elements related to curriculum, production of something new, and transformations emerge in classrooms resulting in educational innovations. Evaluations are necessary to improve the innovations so that successful methods can be implemented in all teaching and learning community in an institution [ 2 ]. The pandemic has forced digital learning and job portal Naukri.com reports a fourfold growth for teaching professionals in the e-learning medium [ 3 ]. The initiatives are taken by the government also focus on online mode as an option in a post-covid world [ 4 ]. A notable learning experience design consultant pointed out that, educators are entrusted to lead the way as the world changes and are actively involved in the transformation [ 5 ]. Weiss notes that an educator needs to make the lectures more interesting [ 6 ].

This paper presents the online teaching–learning tools, methods, and a survey on the innovative practices in teaching and learning. Advantages and obstacles in online teaching, various components on the effective use of online tools, team-based collaborative learning, simulation, and animation-based learning are discussed in detail. The outcome of a survey on the effectiveness of online teaching and learning is included. The following sections present the online teaching–learning tools, the details of the questionnaire used for the survey, and the outcome of the survey.

Online Teaching and Learning Tools

The four essential parts of online teaching [ 7 ] are virtual classrooms, individual activities, assessments in real-time, and collaborative group work. Online teaching tools are used to facilitate faculty-student interaction as well as student–student collaborations [ 8 ]. The ease of use, the satisfaction level, the usefulness, and the confidence level of the instructor is crucial [ 9 ] in motivating the instructor to use online teaching tools. Higher education institutes recognize the need to accommodate wide diverse learners and Hilliard [ 10 ] points out that technical support and awareness to both faculty and student is essential in the age of blended learning. Data analytics tool coupled with the LMS is essential to enhance [ 11 ] the quality of teaching and improve the course design. The effective usage of online tools is depicted in Fig. 1 comprising of an instructor to student delivery, collaboration among students, training for the tools, and data analytics for constant improvement of course and assessment methods.

The various components of effective usage of online tools

Online Teaching Tools

A plethora of online teaching tools are available and this poses a challenge for decision-makers to choose the tools that best suits the needs of the course. The need for the tools, the cost, usability, and features determine which tools are adopted by various learners and institutions. Many universities have offered online classes for students. These are taken up by students opting for part-time courses. This offers them flexibility in timing and eliminates the need for travel to campus. The pandemic situation in 2019 has forced many if not all institutions to completely shift classes online. LMS tools are packaged as Software as a Service (SaaS) and the pricing generally falls into 4 categories: (i) per learner, per month (ii) per learner, per use (iii) per course (iv) licensing fee for on-premise installation [ 12 ].

Online Learning Tools

Online teaching/learning as part of the ongoing semester is typically part of a classroom management tool. GSuite for education [ 13 ] and Microsoft Teams [ 14 ] are both widely adopted by schools and colleges during the COVID-19 pandemic to effectively shift regular classes online. Other popular learning management systems that have been adopted as part of blended learning are Edmodo [ 15 ], Blackboard [ 16 ], and MoodleCloud [ 17 ]. Davis et al. [ 18 ] point out advantages and obstacles for both students and instructors about online teaching shown in Table 1 .

The effectiveness of course delivery depends on using the appropriate tools in the course design. This involves engaging the learners and modifying the course design to cater to various learning styles.

A Survey on Innovative Practices in Teaching and Learning

The questionnaire aims to identify the effectiveness of various online tools and technologies, the preferred learning methods of students, and other factors that might influence the teaching–learning process. The parameters were based on different types of learners, advantages, and obstacles to online learning [ 10 , 18 ]. Questions 1–4 are used to comprehend the learning style of the student. Questions 5–7 are posed to find out the effectiveness of the medium used for teaching and evaluation. Questions 8–12 are framed to identify the various barriers to online learning faced by students.

This methodology is adopted as most of the students are attending online courses from home and polls of this kind will go well with the students from various universities. Students participated in the survey and answered most of the questionnaire enthusiastically. The only challenge was a suitable environment and free time for them to answer the questionnaire, as they are already loaded with lots of online work. Students from various universities pursuing professional courses like engineering and medicine took part in this survey. They are from various branches of sciences and technologies. Students are from private universities, colleges, and government institutions. Figure 2 shows the institution-wise respondents. Microsoft Teams and Google meet platforms were used for this survey among university, medical college, and engineering college students. About 450 students responded to this survey. 52% of the respondents are from VIT University Vellore, Tamil Nadu, 23% of the respondents are from CMR Institute of Technology (CMRIT), Bangalore, 15% of the respondents are from medical colleges and 10% are from other engineering colleges. During this pandemic period, VIT students are staying with parents who are living in different states of India like Andhra, Telangana, Kerala, Karnataka, MP, Haryana, Punjab, Maharashtra, Andaman, and so on. Only a few students are living in Tamil Nadu. Some of the students are staying with parents in other countries like Dubai, Oman, South Africa, and so on. Some of the students of CMRIT Bangalore are living in Bangalore and others in towns and villages of Karnataka state. Students of medical colleges are living in different parts of Tamil Nadu and students of engineering colleges are living in different parts of Andhra Pradesh. Hence, the survey is done in a wider geographical region.

Institution-wise respondents

Figure 3 shows the branch-wise respondents. It is shown that 158 students belong to mechanical/civil engineering. 108 respondents belong to computer science and engineering, 68 students belong to medicine, 58 students belong to electrical & electronics engineering, and electronics & communication engineering. 58 students belong to other disciplines.

Branch-wise respondents

Questionnaire Used

Students were assured of their confidentiality and were promised that their names would not appear in the document. A list of the questions asked as part of the survey is given below.

Questionnaire:

Sample group: B Tech students from different branches of sciences across various engineering institutions and MBBS medical students.

Which of the methods engage you personally to learn digitally ?

Individual assignment

Small group (No. 5 students) work

Large group (No. 10 students and more) work

Project-based learning

Which of the digital collaborations enables you to work on a specific task at ease

Two by two (2 member team)

Small group workgroup (No. 5 students) work

Which of the digital approaches motivate you to learn

Whiteboard and pen

PowerPoint presentation

Digital pen and slate

My experience with online learning from home digitally

I am learning at my own pace comfortably

My situational challenges are not suitable

I can learn better with uninterrupted network connectivity

I am distracted with various activities at home, viz. TV, chatting, etc.

Which type of recorded video lecture is more effective for learning ?

delivered by my faculty

delivered by NPTEL

delivered by reputed Overseas Universities

delivered by unknown experts

Which type of quiz is more effective for testing the understanding?

Traditional—pen and paper—MCQ

Traditional—pen and paper—short answers

Online quiz—MCQ

Online quiz—short answers

Student version software downloaded from the internet is useful for learning

Unable to decide

Online teaching – learning takes place effectively because:

Every student can hear the lecture clearly

PPTs are available right in front of every student

Students can ask doubts without much reservation

Students need not walk long distances before reaching the class

Which of the following statements is true of online learning off-campus ?

No one disturbs me during my online learning.

My friend/family member/roommate/neighbor occasionally disturb me

My friend/family member/roommate/neighbor constantly disturb me

At home/place of residence, how many responsibilities do you have?

I don’t have many responsibilities.

I have a moderate amount of responsibilities, but I have sufficient time for online learning.

I have many responsibilities; I don’t have any time left for online learning.

What is your most preferred method for clearing doubts in online learning?

Ask the professor during/after an online lecture

Post the query in a discussion forum of your class and get help from your peers

Go through online material providing an additional explanation.

Which of the following devices do you use for your online learning?

A laptop/desktop computer

A smartphone

Other devices

Outcome of the survey

Students would prefer to work in a group of 5 students to engage personally in digital learning as seen from Fig. 4 .

Personal engagement in digital learning

Digital collaboration to enable students to work at ease on a specific task is to allow them to work in small groups of 5 students as seen in Fig. 5 .

Digital collaboration to enable students to work at ease

Animations are found to be the best digital approach motivating many students to learn as seen in Fig. 6 .

Digital approaches that motivate students to learn

The online learning experience of students is shown in Fig. 7 . The majority of students have said that they can learn at their own pace comfortably through online learning.

The online learning experience of students

The effectiveness of the recorded video lecture is shown in Fig. 8 . The majority of students agree that the video lectures delivered by his/her faculty teaching the subject help students to learn effectively.

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Darius, P.S.H., Gundabattini, E. & Solomon, D.G. A Survey on the Effectiveness of Online Teaching–Learning Methods for University and College Students. J. Inst. Eng. India Ser. B 102 , 1325–1334 (2021). https://doi.org/10.1007/s40031-021-00581-x

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Research Article

COVID-19’s impacts on the scope, effectiveness, and interaction characteristics of online learning: A social network analysis

Roles Data curation, Formal analysis, Methodology, Writing – review & editing

¶ ‡ JZ and YD are contributed equally to this work as first authors.

Affiliation School of Educational Information Technology, South China Normal University, Guangzhou, Guangdong, China

Roles Data curation, Formal analysis, Methodology, Writing – original draft

Affiliations School of Educational Information Technology, South China Normal University, Guangzhou, Guangdong, China, Hangzhou Zhongce Vocational School Qiantang, Hangzhou, Zhejiang, China

Roles Data curation, Writing – original draft

Roles Data curation

Roles Writing – original draft

Affiliation Faculty of Education, Shenzhen University, Shenzhen, Guangdong, China

Roles Conceptualization, Supervision, Writing – review & editing

* E-mail: [email protected] (JH); [email protected] (YZ)

Junyi Zhang,
Yigang Ding,
Xinru Yang,
Jinping Zhong,
XinXin Qiu,
Zhishan Zou,
Yujie Xu,
Xiunan Jin,
Xiaomin Wu,

Published: August 23, 2022
https://doi.org/10.1371/journal.pone.0273016
Reader Comments

The COVID-19 outbreak brought online learning to the forefront of education. Scholars have conducted many studies on online learning during the pandemic, but only a few have performed quantitative comparative analyses of students’ online learning behavior before and after the outbreak. We collected review data from China’s massive open online course platform called icourse.163 and performed social network analysis on 15 courses to explore courses’ interaction characteristics before, during, and after the COVID-19 pan-demic. Specifically, we focused on the following aspects: (1) variations in the scale of online learning amid COVID-19; (2a) the characteristics of online learning interaction during the pandemic; (2b) the characteristics of online learning interaction after the pandemic; and (3) differences in the interaction characteristics of social science courses and natural science courses. Results revealed that only a small number of courses witnessed an uptick in online interaction, suggesting that the pandemic’s role in promoting the scale of courses was not significant. During the pandemic, online learning interaction became more frequent among course network members whose interaction scale increased. After the pandemic, although the scale of interaction declined, online learning interaction became more effective. The scale and level of interaction in Electrodynamics (a natural science course) and Economics (a social science course) both rose during the pan-demic. However, long after the pandemic, the Economics course sustained online interaction whereas interaction in the Electrodynamics course steadily declined. This discrepancy could be due to the unique characteristics of natural science courses and social science courses.

Citation: Zhang J, Ding Y, Yang X, Zhong J, Qiu X, Zou Z, et al. (2022) COVID-19’s impacts on the scope, effectiveness, and interaction characteristics of online learning: A social network analysis. PLoS ONE 17(8): e0273016. https://doi.org/10.1371/journal.pone.0273016

Editor: Heng Luo, Central China Normal University, CHINA

Received: April 20, 2022; Accepted: July 29, 2022; Published: August 23, 2022

Copyright: © 2022 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data underlying the results presented in the study were downloaded from https://www.icourse163.org/ and are now shared fully on Github ( https://github.com/zjyzhangjunyi/dataset-from-icourse163-for-SNA ). These data have no private information and can be used for academic research free of charge.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

The development of the mobile internet has spurred rapid advances in online learning, offering novel prospects for teaching and learning and a learning experience completely different from traditional instruction. Online learning harnesses the advantages of network technology and multimedia technology to transcend the boundaries of conventional education [ 1 ]. Online courses have become a popular learning mode owing to their flexibility and openness. During online learning, teachers and students are in different physical locations but interact in multiple ways (e.g., via online forum discussions and asynchronous group discussions). An analysis of online learning therefore calls for attention to students’ participation. Alqurashi [ 2 ] defined interaction in online learning as the process of constructing meaningful information and thought exchanges between more than two people; such interaction typically occurs between teachers and learners, learners and learners, and the course content and learners.

Massive open online courses (MOOCs), a 21st-century teaching mode, have greatly influenced global education. Data released by China’s Ministry of Education in 2020 show that the country ranks first globally in the number and scale of higher education MOOCs. The COVID-19 outbreak has further propelled this learning mode, with universities being urged to leverage MOOCs and other online resource platforms to respond to government’s “School’s Out, But Class’s On” policy [ 3 ]. Besides MOOCs, to reduce in-person gatherings and curb the spread of COVID-19, various online learning methods have since become ubiquitous [ 4 ]. Though Lederman asserted that the COVID-19 outbreak has positioned online learning technologies as the best way for teachers and students to obtain satisfactory learning experiences [ 5 ], it remains unclear whether the COVID-19 pandemic has encouraged interaction in online learning, as interactions between students and others play key roles in academic performance and largely determine the quality of learning experiences [ 6 ]. Similarly, it is also unclear what impact the COVID-19 pandemic has had on the scale of online learning.

Social constructivism paints learning as a social phenomenon. As such, analyzing the social structures or patterns that emerge during the learning process can shed light on learning-based interaction [ 7 ]. Social network analysis helps to explain how a social network, rooted in interactions between learners and their peers, guides individuals’ behavior, emotions, and outcomes. This analytical approach is especially useful for evaluating interactive relationships between network members [ 8 ]. Mohammed cited social network analysis (SNA) as a method that can provide timely information about students, learning communities and interactive networks. SNA has been applied in numerous fields, including education, to identify the number and characteristics of interelement relationships. For example, Lee et al. also used SNA to explore the effects of blogs on peer relationships [ 7 ]. Therefore, adopting SNA to examine interactions in online learning communities during the COVID-19 pandemic can uncover potential issues with this online learning model.

Taking China’s icourse.163 MOOC platform as an example, we chose 15 courses with a large number of participants for SNA, focusing on learners’ interaction characteristics before, during, and after the COVID-19 outbreak. We visually assessed changes in the scale of network interaction before, during, and after the outbreak along with the characteristics of interaction in Gephi. Examining students’ interactions in different courses revealed distinct interactive network characteristics, the pandemic’s impact on online courses, and relevant suggestions. Findings are expected to promote effective interaction and deep learning among students in addition to serving as a reference for the development of other online learning communities.

2. Literature review and research questions

Interaction is deemed as central to the educational experience and is a major focus of research on online learning. Moore began to study the problem of interaction in distance education as early as 1989. He defined three core types of interaction: student–teacher, student–content, and student–student [ 9 ]. Lear et al. [ 10 ] described an interactivity/ community-process model of distance education: they specifically discussed the relationships between interactivity, community awareness, and engaging learners and found interactivity and community awareness to be correlated with learner engagement. Zulfikar et al. [ 11 ] suggested that discussions initiated by the students encourage more students’ engagement than discussions initiated by the instructors. It is most important to afford learners opportunities to interact purposefully with teachers, and improving the quality of learner interaction is crucial to fostering profound learning [ 12 ]. Interaction is an important way for learners to communicate and share information, and a key factor in the quality of online learning [ 13 ].

Timely feedback is the main component of online learning interaction. Woo and Reeves discovered that students often become frustrated when they fail to receive prompt feedback [ 14 ]. Shelley et al. conducted a three-year study of graduate and undergraduate students’ satisfaction with online learning at universities and found that interaction with educators and students is the main factor affecting satisfaction [ 15 ]. Teachers therefore need to provide students with scoring justification, support, and constructive criticism during online learning. Some researchers examined online learning during the COVID-19 pandemic. They found that most students preferred face-to-face learning rather than online learning due to obstacles faced online, such as a lack of motivation, limited teacher-student interaction, and a sense of isolation when learning in different times and spaces [ 16 , 17 ]. However, it can be reduced by enhancing the online interaction between teachers and students [ 18 ].

Research showed that interactions contributed to maintaining students’ motivation to continue learning [ 19 ]. Baber argued that interaction played a key role in students’ academic performance and influenced the quality of the online learning experience [ 20 ]. Hodges et al. maintained that well-designed online instruction can lead to unique teaching experiences [ 21 ]. Banna et al. mentioned that using discussion boards, chat sessions, blogs, wikis, and other tools could promote student interaction and improve participation in online courses [ 22 ]. During the COVID-19 pandemic, Mahmood proposed a series of teaching strategies suitable for distance learning to improve its effectiveness [ 23 ]. Lapitan et al. devised an online strategy to ease the transition from traditional face-to-face instruction to online learning [ 24 ]. The preceding discussion suggests that online learning goes beyond simply providing learning resources; teachers should ideally design real-life activities to give learners more opportunities to participate.

As mentioned, COVID-19 has driven many scholars to explore the online learning environment. However, most have ignored the uniqueness of online learning during this time and have rarely compared pre- and post-pandemic online learning interaction. Taking China’s icourse.163 MOOC platform as an example, we chose 15 courses with a large number of participants for SNA, centering on student interaction before and after the pandemic. Gephi was used to visually analyze changes in the scale and characteristics of network interaction. The following questions were of particular interest:

(1) Can the COVID-19 pandemic promote the expansion of online learning?
(2a) What are the characteristics of online learning interaction during the pandemic?
(2b) What are the characteristics of online learning interaction after the pandemic?
(3) How do interaction characteristics differ between social science courses and natural science courses?

3. Methodology

3.1 research context.

We selected several courses with a large number of participants and extensive online interaction among hundreds of courses on the icourse.163 MOOC platform. These courses had been offered on the platform for at least three semesters, covering three periods (i.e., before, during, and after the COVID-19 outbreak). To eliminate the effects of shifts in irrelevant variables (e.g., course teaching activities), we chose several courses with similar teaching activities and compared them on multiple dimensions. All course content was taught online. The teachers of each course posted discussion threads related to learning topics; students were expected to reply via comments. Learners could exchange ideas freely in their responses in addition to asking questions and sharing their learning experiences. Teachers could answer students’ questions as well. Conversations in the comment area could partly compensate for a relative absence of online classroom interaction. Teacher–student interaction is conducive to the formation of a social network structure and enabled us to examine teachers’ and students’ learning behavior through SNA. The comment areas in these courses were intended for learners to construct knowledge via reciprocal communication. Meanwhile, by answering students’ questions, teachers could encourage them to reflect on their learning progress. These courses’ successive terms also spanned several phases of COVID-19, allowing us to ascertain the pandemic’s impact on online learning.

3.2 Data collection and preprocessing

To avoid interference from invalid or unclear data, the following criteria were applied to select representative courses: (1) generality (i.e., public courses and professional courses were chosen from different schools across China); (2) time validity (i.e., courses were held before during, and after the pandemic); and (3) notability (i.e., each course had at least 2,000 participants). We ultimately chose 15 courses across the social sciences and natural sciences (see Table 1 ). The coding is used to represent the course name.

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https://doi.org/10.1371/journal.pone.0273016.t001

To discern courses’ evolution during the pandemic, we gathered data on three terms before, during, and after the COVID-19 outbreak in addition to obtaining data from two terms completed well before the pandemic and long after. Our final dataset comprised five sets of interactive data. Finally, we collected about 120,000 comments for SNA. Because each course had a different start time—in line with fluctuations in the number of confirmed COVID-19 cases in China and the opening dates of most colleges and universities—we divided our sample into five phases: well before the pandemic (Phase I); before the pandemic (Phase Ⅱ); during the pandemic (Phase Ⅲ); after the pandemic (Phase Ⅳ); and long after the pandemic (Phase Ⅴ). We sought to preserve consistent time spans to balance the amount of data in each period ( Fig 1 ).

https://doi.org/10.1371/journal.pone.0273016.g001

3.3 Instrumentation

Participants’ comments and “thumbs-up” behavior data were converted into a network structure and compared using social network analysis (SNA). Network analysis, according to M’Chirgui, is an effective tool for clarifying network relationships by employing sophisticated techniques [ 25 ]. Specifically, SNA can help explain the underlying relationships among team members and provide a better understanding of their internal processes. Yang and Tang used SNA to discuss the relationship between team structure and team performance [ 26 ]. Golbeck argued that SNA could improve the understanding of students’ learning processes and reveal learners’ and teachers’ role dynamics [ 27 ].

To analyze Question (1), the number of nodes and diameter in the generated network were deemed as indicators of changes in network size. Social networks are typically represented as graphs with nodes and degrees, and node count indicates the sample size [ 15 ]. Wellman et al. proposed that the larger the network scale, the greater the number of network members providing emotional support, goods, services, and companionship [ 28 ]. Jan’s study measured the network size by counting the nodes which represented students, lecturers, and tutors [ 29 ]. Similarly, network nodes in the present study indicated how many learners and teachers participated in the course, with more nodes indicating more participants. Furthermore, we investigated the network diameter, a structural feature of social networks, which is a common metric for measuring network size in SNA [ 30 ]. The network diameter refers to the longest path between any two nodes in the network. There has been evidence that a larger network diameter leads to greater spread of behavior [ 31 ]. Likewise, Gašević et al. found that larger networks were more likely to spread innovative ideas about educational technology when analyzing MOOC-related research citations [ 32 ]. Therefore, we employed node count and network diameter to measure the network’s spatial size and further explore the expansion characteristic of online courses. Brief introduction of these indicators can be summarized in Table 2 .

https://doi.org/10.1371/journal.pone.0273016.t002

To address Question (2), a list of interactive analysis metrics in SNA were introduced to scrutinize learners’ interaction characteristics in online learning during and after the pandemic, as shown below:

(1) The average degree reflects the density of the network by calculating the average number of connections for each node. As Rong and Xu suggested, the average degree of a network indicates how active its participants are [ 33 ]. According to Hu, a higher average degree implies that more students are interacting directly with each other in a learning context [ 34 ]. The present study inherited the concept of the average degree from these previous studies: the higher the average degree, the more frequent the interaction between individuals in the network.
(2) Essentially, a weighted average degree in a network is calculated by multiplying each degree by its respective weight, and then taking the average. Bydžovská took the strength of the relationship into account when determining the weighted average degree [ 35 ]. By calculating friendship’s weighted value, Maroulis assessed peer achievement within a small-school reform [ 36 ]. Accordingly, we considered the number of interactions as the weight of the degree, with a higher average degree indicating more active interaction among learners.
(3) Network density is the ratio between actual connections and potential connections in a network. The more connections group members have with each other, the higher the network density. In SNA, network density is similar to group cohesion, i.e., a network of more strong relationships is more cohesive [ 37 ]. Network density also reflects how much all members are connected together [ 38 ]. Therefore, we adopted network density to indicate the closeness among network members. Higher network density indicates more frequent interaction and closer communication among students.
(4) Clustering coefficient describes local network attributes and indicates that two nodes in the network could be connected through adjacent nodes. The clustering coefficient measures users’ tendency to gather (cluster) with others in the network: the higher the clustering coefficient, the more frequently users communicate with other group members. We regarded this indicator as a reflection of the cohesiveness of the group [ 39 ].
(5) In a network, the average path length is the average number of steps along the shortest paths between any two nodes. Oliveres has observed that when an average path length is small, the route from one node to another is shorter when graphed [ 40 ]. This is especially true in educational settings where students tend to become closer friends. So we consider that the smaller the average path length, the greater the possibility of interaction between individuals in the network.
(6) A network with a large number of nodes, but whose average path length is surprisingly small, is known as the small-world effect [ 41 ]. A higher clustering coefficient and shorter average path length are important indicators of a small-world network: a shorter average path length enables the network to spread information faster and more accurately; a higher clustering coefficient can promote frequent knowledge exchange within the group while boosting the timeliness and accuracy of knowledge dissemination [ 42 ]. Brief introduction of these indicators can be summarized in Table 3 .

https://doi.org/10.1371/journal.pone.0273016.t003

To analyze Question 3, we used the concept of closeness centrality, which determines how close a vertex is to others in the network. As Opsahl et al. explained, closeness centrality reveals how closely actors are coupled with their entire social network [ 43 ]. In order to analyze social network-based engineering education, Putnik et al. examined closeness centrality and found that it was significantly correlated with grades [ 38 ]. We used closeness centrality to measure the position of an individual in the network. Brief introduction of these indicators can be summarized in Table 4 .

https://doi.org/10.1371/journal.pone.0273016.t004

3.4 Ethics statement

This study was approved by the Academic Committee Office (ACO) of South China Normal University ( http://fzghb.scnu.edu.cn/ ), Guangzhou, China. Research data were collected from the open platform and analyzed anonymously. There are thus no privacy issues involved in this study.

4.1 COVID-19’s role in promoting the scale of online courses was not as important as expected

As shown in Fig 2 , the number of course participants and nodes are closely correlated with the pandemic’s trajectory. Because the number of participants in each course varied widely, we normalized the number of participants and nodes to more conveniently visualize course trends. Fig 2 depicts changes in the chosen courses’ number of participants and nodes before the pandemic (Phase II), during the pandemic (Phase III), and after the pandemic (Phase IV). The number of participants in most courses during the pandemic exceeded those before and after the pandemic. But the number of people who participate in interaction in some courses did not increase.

https://doi.org/10.1371/journal.pone.0273016.g002

In order to better analyze the trend of interaction scale in online courses before, during, and after the pandemic, the selected courses were categorized according to their scale change. When the number of participants increased (decreased) beyond 20% (statistical experience) and the diameter also increased (decreased), the course scale was determined to have increased (decreased); otherwise, no significant change was identified in the course’s interaction scale. Courses were subsequently divided into three categories: increased interaction scale, decreased interaction scale, and no significant change. Results appear in Table 5 .

https://doi.org/10.1371/journal.pone.0273016.t005

From before the pandemic until it broke out, the interaction scale of five courses increased, accounting for 33.3% of the full sample; one course’s interaction scale declined, accounting for 6.7%. The interaction scale of nine courses decreased, accounting for 60%. The pandemic’s role in promoting online courses thus was not as important as anticipated, and most courses’ interaction scale did not change significantly throughout.

No courses displayed growing interaction scale after the pandemic: the interaction scale of nine courses fell, accounting for 60%; and the interaction scale of six courses did not shift significantly, accounting for 40%. Courses with an increased scale of interaction during the pandemic did not maintain an upward trend. On the contrary, the improvement in the pandemic caused learners’ enthusiasm for online learning to wane. We next analyzed several interaction metrics to further explore course interaction during different pandemic periods.

4.2 Characteristics of online learning interaction amid COVID-19

4.2.1 during the covid-19 pandemic, online learning interaction in some courses became more active..

Changes in course indicators with the growing interaction scale during the pandemic are presented in Fig 3 , including SS5, SS6, NS1, NS3, and NS8. The horizontal ordinate indicates the number of courses, with red color representing the rise of the indicator value on the vertical ordinate and blue representing the decline.

https://doi.org/10.1371/journal.pone.0273016.g003

Specifically: (1) The average degree and weighted average degree of the five course networks demonstrated an upward trend. The emergence of the pandemic promoted students’ enthusiasm; learners were more active in the interactive network. (2) Fig 3 shows that 3 courses had increased network density and 2 courses had decreased. The higher the network density, the more communication within the team. Even though the pandemic accelerated the interaction scale and frequency, the tightness between learners in some courses did not improve. (3) The clustering coefficient of social science courses rose whereas the clustering coefficient and small-world property of natural science courses fell. The higher the clustering coefficient and the small-world property, the better the relationship between adjacent nodes and the higher the cohesion [ 39 ]. (4) Most courses’ average path length increased as the interaction scale increased. However, when the average path length grew, adverse effects could manifest: communication between learners might be limited to a small group without multi-directional interaction.

When the pandemic emerged, the only declining network scale belonged to a natural science course (NS2). The change in each course index is pictured in Fig 4 . The abscissa indicates the size of the value, with larger values to the right. The red dot indicates the index value before the pandemic; the blue dot indicates its value during the pandemic. If the blue dot is to the right of the red dot, then the value of the index increased; otherwise, the index value declined. Only the weighted average degree of the course network increased. The average degree, network density decreased, indicating that network members were not active and that learners’ interaction degree and communication frequency lessened. Despite reduced learner interaction, the average path length was small and the connectivity between learners was adequate.

https://doi.org/10.1371/journal.pone.0273016.g004

4.2.2 After the COVID-19 pandemic, the scale decreased rapidly, but most course interaction was more effective.

Fig 5 shows the changes in various courses’ interaction indicators after the pandemic, including SS1, SS2, SS3, SS6, SS7, NS2, NS3, NS7, and NS8.

https://doi.org/10.1371/journal.pone.0273016.g005

Specifically: (1) The average degree and weighted average degree of most course networks decreased. The scope and intensity of interaction among network members declined rapidly, as did learners’ enthusiasm for communication. (2) The network density of seven courses also fell, indicating weaker connections between learners in most courses. (3) In addition, the clustering coefficient and small-world property of most course networks decreased, suggesting little possibility of small groups in the network. The scope of interaction between learners was not limited to a specific space, and the interaction objects had no significant tendencies. (4) Although the scale of course interaction became smaller in this phase, the average path length of members’ social networks shortened in nine courses. Its shorter average path length would expedite the spread of information within the network as well as communication and sharing among network members.

Fig 6 displays the evolution of course interaction indicators without significant changes in interaction scale after the pandemic, including SS4, SS5, NS1, NS4, NS5, and NS6.

https://doi.org/10.1371/journal.pone.0273016.g006

Specifically: (1) Some course members’ social networks exhibited an increase in the average and weighted average. In these cases, even though the course network’s scale did not continue to increase, communication among network members rose and interaction became more frequent and deeper than before. (2) Network density and average path length are indicators of social network density. The greater the network density, the denser the social network; the shorter the average path length, the more concentrated the communication among network members. However, at this phase, the average path length and network density in most courses had increased. Yet the network density remained small despite having risen ( Table 6 ). Even with more frequent learner interaction, connections remained distant and the social network was comparatively sparse.

https://doi.org/10.1371/journal.pone.0273016.t006

In summary, the scale of interaction did not change significantly overall. Nonetheless, some course members’ frequency and extent of interaction increased, and the relationships between network members became closer as well. In the study, we found it interesting that the interaction scale of Economics (a social science course) course and Electrodynamics (a natural science course) course expanded rapidly during the pandemic and retained their interaction scale thereafter. We next assessed these two courses to determine whether their level of interaction persisted after the pandemic.

4.3 Analyses of natural science courses and social science courses

4.3.1 analyses of the interaction characteristics of economics and electrodynamics..

Economics and Electrodynamics are social science courses and natural science courses, respectively. Members’ interaction within these courses was similar: the interaction scale increased significantly when COVID-19 broke out (Phase Ⅲ), and no significant changes emerged after the pandemic (Phase Ⅴ). We hence focused on course interaction long after the outbreak (Phase V) and compared changes across multiple indicators, as listed in Table 7 .

https://doi.org/10.1371/journal.pone.0273016.t007

As the pandemic continued to improve, the number of participants and the diameter long after the outbreak (Phase V) each declined for Economics compared with after the pandemic (Phase IV). The interaction scale decreased, but the interaction between learners was much deeper. Specifically: (1) The weighted average degree, network density, clustering coefficient, and small-world property each reflected upward trends. The pandemic therefore exerted a strong impact on this course. Interaction was well maintained even after the pandemic. The smaller network scale promoted members’ interaction and communication. (2) Compared with after the pandemic (Phase IV), members’ network density increased significantly, showing that relationships between learners were closer and that cohesion was improving. (3) At the same time, as the clustering coefficient and small-world property grew, network members demonstrated strong small-group characteristics: the communication between them was deepening and their enthusiasm for interaction was higher. (4) Long after the COVID-19 outbreak (Phase V), the average path length was reduced compared with previous terms, knowledge flowed more quickly among network members, and the degree of interaction gradually deepened.

The average degree, weighted average degree, network density, clustering coefficient, and small-world property of Electrodynamics all decreased long after the COVID-19 outbreak (Phase V) and were lower than during the outbreak (Phase Ⅲ). The level of learner interaction therefore gradually declined long after the outbreak (Phase V), and connections between learners were no longer active. Although the pandemic increased course members’ extent of interaction, this rise was merely temporary: students’ enthusiasm for learning waned rapidly and their interaction decreased after the pandemic (Phase IV). To further analyze the interaction characteristics of course members in Economics and Electrodynamics, we evaluated the closeness centrality of their social networks, as shown in section 4.3.2.

4.3.2 Analysis of the closeness centrality of Economics and Electrodynamics.

The change in the closeness centrality of social networks in Economics was small, and no sharp upward trend appeared during the pandemic outbreak, as shown in Fig 7 . The emergence of COVID-19 apparently fostered learners’ interaction in Economics albeit without a significant impact. The closeness centrality changed in Electrodynamics varied from that of Economics: upon the COVID-19 outbreak, closeness centrality was significantly different from other semesters. Communication between learners was closer and interaction was more effective. Electrodynamics course members’ social network proximity decreased rapidly after the pandemic. Learners’ communication lessened. In general, Economics course showed better interaction before the outbreak and was less affected by the pandemic; Electrodynamics course was more affected by the pandemic and showed different interaction characteristics at different periods of the pandemic.

(Note: "****" indicates the significant distinction in closeness centrality between the two periods, otherwise no significant distinction).

https://doi.org/10.1371/journal.pone.0273016.g007

5. Discussion

We referred to discussion forums from several courses on the icourse.163 MOOC platform to compare online learning before, during, and after the COVID-19 pandemic via SNA and to delineate the pandemic’s effects on online courses. Only 33.3% of courses in our sample increased in terms of interaction during the pandemic; the scale of interaction did not rise in any courses thereafter. When the courses scale rose, the scope and frequency of interaction showed upward trends during the pandemic; and the clustering coefficient of natural science courses and social science courses differed: the coefficient for social science courses tended to rise whereas that for natural science courses generally declined. When the pandemic broke out, the interaction scale of a single natural science course decreased along with its interaction scope and frequency. The amount of interaction in most courses shrank rapidly during the pandemic and network members were not as active as they had been before. However, after the pandemic, some courses saw declining interaction but greater communication between members; interaction also became more frequent and deeper than before.

5.1 During the COVID-19 pandemic, the scale of interaction increased in only a few courses

The pandemic outbreak led to a rapid increase in the number of participants in most courses; however, the change in network scale was not significant. The scale of online interaction expanded swiftly in only a few courses; in others, the scale either did not change significantly or displayed a downward trend. After the pandemic, the interaction scale in most courses decreased quickly; the same pattern applied to communication between network members. Learners’ enthusiasm for online interaction reduced as the circumstances of the pandemic improved—potentially because, during the pandemic, China’s Ministry of Education declared “School’s Out, But Class’s On” policy. Major colleges and universities were encouraged to use the Internet and informational resources to provide learning support, hence the sudden increase in the number of participants and interaction in online courses [ 46 ]. After the pandemic, students’ enthusiasm for online learning gradually weakened, presumably due to easing of the pandemic [ 47 ]. More activities also transitioned from online to offline, which tempered learners’ online discussion. Research has shown that long-term online learning can even bore students [ 48 ].

Most courses’ interaction scale decreased significantly after the pandemic. First, teachers and students occupied separate spaces during the outbreak, had few opportunities for mutual cooperation and friendship, and lacked a sense of belonging [ 49 ]. Students’ enthusiasm for learning dissipated over time [ 50 ]. Second, some teachers were especially concerned about adapting in-person instructional materials for digital platforms; their pedagogical methods were ineffective, and they did not provide learning activities germane to student interaction [ 51 ]. Third, although teachers and students in remote areas were actively engaged in online learning, some students could not continue to participate in distance learning due to inadequate technology later in the outbreak [ 52 ].

5.2 Characteristics of online learning interaction during and after the COVID-19 pandemic

5.2.1 during the covid-19 pandemic, online interaction in most courses did not change significantly..

The interaction scale of only a few courses increased during the pandemic. The interaction scope and frequency of these courses climbed as well. Yet even as the degree of network interaction rose, course network density did not expand in all cases. The pandemic sparked a surge in the number of online learners and a rapid increase in network scale, but students found it difficult to interact with all learners. Yau pointed out that a greater network scale did not enrich the range of interaction between individuals; rather, the number of individuals who could interact directly was limited [ 53 ]. The internet facilitates interpersonal communication. However, not everyone has the time or ability to establish close ties with others [ 54 ].

In addition, social science courses and natural science courses in our sample revealed disparate trends in this regard: the clustering coefficient of social science courses increased and that of natural science courses decreased. Social science courses usually employ learning approaches distinct from those in natural science courses [ 55 ]. Social science courses emphasize critical and innovative thinking along with personal expression [ 56 ]. Natural science courses focus on practical skills, methods, and principles [ 57 ]. Therefore, the content of social science courses can spur large-scale discussion among learners. Some course evaluations indicated that the course content design was suboptimal as well: teachers paid close attention to knowledge transmission and much less to piquing students’ interest in learning. In addition, the thread topics that teachers posted were scarcely diversified and teachers’ questions lacked openness. These attributes could not spark active discussion among learners.

5.2.2 Online learning interaction declined after the COVID-19 pandemic.

Most courses’ interaction scale and intensity decreased rapidly after the pandemic, but some did not change. Courses with a larger network scale did not continue to expand after the outbreak, and students’ enthusiasm for learning paled. The pandemic’s reduced severity also influenced the number of participants in online courses. Meanwhile, restored school order moved many learning activities from virtual to in-person spaces. Face-to-face learning has gradually replaced online learning, resulting in lower enrollment and less interaction in online courses. Prolonged online courses could have also led students to feel lonely and to lack a sense of belonging [ 58 ].

The scale of interaction in some courses did not change substantially after the pandemic yet learners’ connections became tighter. We hence recommend that teachers seize pandemic-related opportunities to design suitable activities. Additionally, instructors should promote student-teacher and student-student interaction, encourage students to actively participate online, and generally intensify the impact of online learning.

5.3 What are the characteristics of interaction in social science courses and natural science courses?

The level of interaction in Economics (a social science course) was significantly higher than that in Electrodynamics (a natural science course), and the small-world property in Economics increased as well. To boost online courses’ learning-related impacts, teachers can divide groups of learners based on the clustering coefficient and the average path length. Small groups of students may benefit teachers in several ways: to participate actively in activities intended to expand students’ knowledge, and to serve as key actors in these small groups. Cultivating students’ keenness to participate in class activities and self-management can also help teachers guide learner interaction and foster deep knowledge construction.

As evidenced by comments posted in the Electrodynamics course, we observed less interaction between students. Teachers also rarely urged students to contribute to conversations. These trends may have arisen because teachers and students were in different spaces. Teachers might have struggled to discern students’ interaction status. Teachers could also have failed to intervene in time, to design online learning activities that piqued learners’ interest, and to employ sound interactive theme planning and guidance. Teachers are often active in traditional classroom settings. Their roles are comparatively weakened online, such that they possess less control over instruction [ 59 ]. Online instruction also requires a stronger hand in learning: teachers should play a leading role in regulating network members’ interactive communication [ 60 ]. Teachers can guide learners to participate, help learners establish social networks, and heighten students’ interest in learning [ 61 ]. Teachers should attend to core members in online learning while also considering edge members; by doing so, all network members can be driven to share their knowledge and become more engaged. Finally, teachers and assistant teachers should help learners develop knowledge, exchange topic-related ideas, pose relevant questions during course discussions, and craft activities that enable learners to interact online [ 62 ]. These tactics can improve the effectiveness of online learning.

As described, network members displayed distinct interaction behavior in Economics and Electrodynamics courses. First, these courses varied in their difficulty: the social science course seemed easier to understand and focused on divergent thinking. Learners were often willing to express their views in comments and to ponder others’ perspectives [ 63 ]. The natural science course seemed more demanding and was oriented around logical thinking and skills [ 64 ]. Second, courses’ content differed. In general, social science courses favor the acquisition of declarative knowledge and creative knowledge compared with natural science courses. Social science courses also entertain open questions [ 65 ]. Natural science courses revolve around principle knowledge, strategic knowledge, and transfer knowledge [ 66 ]. Problems in these courses are normally more complicated than those in social science courses. Third, the indicators affecting students’ attitudes toward learning were unique. Guo et al. discovered that “teacher feedback” most strongly influenced students’ attitudes towards learning social science courses but had less impact on students in natural science courses [ 67 ]. Therefore, learners in social science courses likely expect more feedback from teachers and greater interaction with others.

6. Conclusion and future work

Our findings show that the network interaction scale of some online courses expanded during the COVID-19 pandemic. The network scale of most courses did not change significantly, demonstrating that the pandemic did not notably alter the scale of course interaction. Online learning interaction among course network members whose interaction scale increased also became more frequent during the pandemic. Once the outbreak was under control, although the scale of interaction declined, the level and scope of some courses’ interactive networks continued to rise; interaction was thus particularly effective in these cases. Overall, the pandemic appeared to have a relatively positive impact on online learning interaction. We considered a pair of courses in detail and found that Economics (a social science course) fared much better than Electrodynamics (a natural science course) in classroom interaction; learners were more willing to partake in-class activities, perhaps due to these courses’ unique characteristics. Brint et al. also came to similar conclusions [ 57 ].

This study was intended to be rigorous. Even so, several constraints can be addressed in future work. The first limitation involves our sample: we focused on a select set of courses hosted on China’s icourse.163 MOOC platform. Future studies should involve an expansive collection of courses to provide a more holistic understanding of how the pandemic has influenced online interaction. Second, we only explored the interactive relationship between learners and did not analyze interactive content. More in-depth content analysis should be carried out in subsequent research. All in all, the emergence of COVID-19 has provided a new path for online learning and has reshaped the distance learning landscape. To cope with associated challenges, educational practitioners will need to continue innovating in online instructional design, strengthen related pedagogy, optimize online learning conditions, and bolster teachers’ and students’ competence in online learning.

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Online and face‐to‐face learning: Evidence from students’ performance during the Covid‐19 pandemic

Carolyn chisadza.

1 Department of Economics, University of Pretoria, Hatfield South Africa

Matthew Clance

Thulani mthembu.

2 Department of Education Innovation, University of Pretoria, Hatfield South Africa

Nicky Nicholls

Eleni yitbarek.

This study investigates the factors that predict students' performance after transitioning from face‐to‐face to online learning as a result of the Covid‐19 pandemic. It uses students' responses from survey questions and the difference in the average assessment grades between pre‐lockdown and post‐lockdown at a South African university. We find that students' performance was positively associated with good wifi access, relative to using mobile internet data. We also observe lower academic performance for students who found transitioning to online difficult and who expressed a preference for self‐study (i.e. reading through class slides and notes) over assisted study (i.e. joining live lectures or watching recorded lectures). The findings suggest that improving digital infrastructure and reducing the cost of internet access may be necessary for mitigating the impact of the Covid‐19 pandemic on education outcomes.

1. INTRODUCTION

The Covid‐19 pandemic has been a wake‐up call to many countries regarding their capacity to cater for mass online education. This situation has been further complicated in developing countries, such as South Africa, who lack the digital infrastructure for the majority of the population. The extended lockdown in South Africa saw most of the universities with mainly in‐person teaching scrambling to source hardware (e.g. laptops, internet access), software (e.g. Microsoft packages, data analysis packages) and internet data for disadvantaged students in order for the semester to recommence. Not only has the pandemic revealed the already stark inequality within the tertiary student population, but it has also revealed that high internet data costs in South Africa may perpetuate this inequality, making online education relatively inaccessible for disadvantaged students. 1

The lockdown in South Africa made it possible to investigate the changes in second‐year students' performance in the Economics department at the University of Pretoria. In particular, we are interested in assessing what factors predict changes in students' performance after transitioning from face‐to‐face (F2F) to online learning. Our main objectives in answering this study question are to establish what study materials the students were able to access (i.e. slides, recordings, or live sessions) and how students got access to these materials (i.e. the infrastructure they used).

The benefits of education on economic development are well established in the literature (Gyimah‐Brempong, 2011 ), ranging from health awareness (Glick et al., 2009 ), improved technological innovations, to increased capacity development and employment opportunities for the youth (Anyanwu, 2013 ; Emediegwu, 2021 ). One of the ways in which inequality is perpetuated in South Africa, and Africa as a whole, is through access to education (Anyanwu, 2016 ; Coetzee, 2014 ; Tchamyou et al., 2019 ); therefore, understanding the obstacles that students face in transitioning to online learning can be helpful in ensuring more equal access to education.

Using students' responses from survey questions and the difference in the average grades between pre‐lockdown and post‐lockdown, our findings indicate that students' performance in the online setting was positively associated with better internet access. Accessing assisted study material, such as narrated slides or recordings of the online lectures, also helped students. We also find lower academic performance for students who reported finding transitioning to online difficult and for those who expressed a preference for self‐study (i.e. reading through class slides and notes) over assisted study (i.e. joining live lectures or watching recorded lectures). The average grades between pre‐lockdown and post‐lockdown were about two points and three points lower for those who reported transitioning to online teaching difficult and for those who indicated a preference for self‐study, respectively. The findings suggest that improving the quality of internet infrastructure and providing assisted learning can be beneficial in reducing the adverse effects of the Covid‐19 pandemic on learning outcomes.

Our study contributes to the literature by examining the changes in the online (post‐lockdown) performance of students and their F2F (pre‐lockdown) performance. This approach differs from previous studies that, in most cases, use between‐subject designs where one group of students following online learning is compared to a different group of students attending F2F lectures (Almatra et al., 2015 ; Brown & Liedholm, 2002 ). This approach has a limitation in that that there may be unobserved characteristics unique to students choosing online learning that differ from those choosing F2F lectures. Our approach avoids this issue because we use a within‐subject design: we compare the performance of the same students who followed F2F learning Before lockdown and moved to online learning during lockdown due to the Covid‐19 pandemic. Moreover, the study contributes to the limited literature that compares F2F and online learning in developing countries.

Several studies that have also compared the effectiveness of online learning and F2F classes encounter methodological weaknesses, such as small samples, not controlling for demographic characteristics, and substantial differences in course materials and assessments between online and F2F contexts. To address these shortcomings, our study is based on a relatively large sample of students and includes demographic characteristics such as age, gender and perceived family income classification. The lecturer and course materials also remained similar in the online and F2F contexts. A significant proportion of our students indicated that they never had online learning experience before. Less than 20% of the students in the sample had previous experience with online learning. This highlights the fact that online education is still relatively new to most students in our sample.

Given the global experience of the fourth industrial revolution (4IR), 2 with rapidly accelerating technological progress, South Africa needs to be prepared for the possibility of online learning becoming the new norm in the education system. To this end, policymakers may consider engaging with various organizations (schools, universities, colleges, private sector, and research facilities) To adopt interventions that may facilitate the transition to online learning, while at the same time ensuring fair access to education for all students across different income levels. 3

1.1. Related literature

Online learning is a form of distance education which mainly involves internet‐based education where courses are offered synchronously (i.e. live sessions online) and/or asynchronously (i.e. students access course materials online in their own time, which is associated with the more traditional distance education). On the other hand, traditional F2F learning is real time or synchronous learning. In a physical classroom, instructors engage with the students in real time, while in the online format instructors can offer real time lectures through learning management systems (e.g. Blackboard Collaborate), or record the lectures for the students to watch later. Purely online courses are offered entirely over the internet, while blended learning combines traditional F2F classes with learning over the internet, and learning supported by other technologies (Nguyen, 2015 ).

Moreover, designing online courses requires several considerations. For example, the quality of the learning environment, the ease of using the learning platform, the learning outcomes to be achieved, instructor support to assist and motivate students to engage with the course material, peer interaction, class participation, type of assessments (Paechter & Maier, 2010 ), not to mention training of the instructor in adopting and introducing new teaching methods online (Lundberg et al., 2008 ). In online learning, instructors are more facilitators of learning. On the other hand, traditional F2F classes are structured in such a way that the instructor delivers knowledge, is better able to gauge understanding and interest of students, can engage in class activities, and can provide immediate feedback on clarifying questions during the class. Additionally, the designing of traditional F2F courses can be less time consuming for instructors compared to online courses (Navarro, 2000 ).

Online learning is also particularly suited for nontraditional students who require flexibility due to work or family commitments that are not usually associated with the undergraduate student population (Arias et al., 2018 ). Initially the nontraditional student belonged to the older adult age group, but with blended learning becoming more commonplace in high schools, colleges and universities, online learning has begun to traverse a wider range of age groups. However, traditional F2F classes are still more beneficial for learners that are not so self‐sufficient and lack discipline in working through the class material in the required time frame (Arias et al., 2018 ).

For the purpose of this literature review, both pure online and blended learning are considered to be online learning because much of the evidence in the literature compares these two types against the traditional F2F learning. The debate in the literature surrounding online learning versus F2F teaching continues to be a contentious one. A review of the literature reveals mixed findings when comparing the efficacy of online learning on student performance in relation to the traditional F2F medium of instruction (Lundberg et al., 2008 ; Nguyen, 2015 ). A number of studies conducted Before the 2000s find what is known today in the empirical literature as the “No Significant Difference” phenomenon (Russell & International Distance Education Certificate Center (IDECC), 1999 ). The seminal work from Russell and IDECC ( 1999 ) involved over 350 comparative studies on online/distance learning versus F2F learning, dating back to 1928. The author finds no significant difference overall between online and traditional F2F classroom education outcomes. Subsequent studies that followed find similar “no significant difference” outcomes (Arbaugh, 2000 ; Fallah & Ubell, 2000 ; Freeman & Capper, 1999 ; Johnson et al., 2000 ; Neuhauser, 2002 ). While Bernard et al. ( 2004 ) also find that overall there is no significant difference in achievement between online education and F2F education, the study does find significant heterogeneity in student performance for different activities. The findings show that students in F2F classes outperform the students participating in synchronous online classes (i.e. classes that require online students to participate in live sessions at specific times). However, asynchronous online classes (i.e. students access class materials at their own time online) outperform F2F classes.

More recent studies find significant results for online learning outcomes in relation to F2F outcomes. On the one hand, Shachar and Yoram ( 2003 ) and Shachar and Neumann ( 2010 ) conduct a meta‐analysis of studies from 1990 to 2009 and find that in 70% of the cases, students taking courses by online education outperformed students in traditionally instructed courses (i.e. F2F lectures). In addition, Navarro and Shoemaker ( 2000 ) observe that learning outcomes for online learners are as effective as or better than outcomes for F2F learners, regardless of background characteristics. In a study on computer science students, Dutton et al. ( 2002 ) find online students perform significantly better compared to the students who take the same course on campus. A meta‐analysis conducted by the US Department of Education finds that students who took all or part of their course online performed better, on average, than those taking the same course through traditional F2F instructions. The report also finds that the effect sizes are larger for studies in which the online learning was collaborative or instructor‐driven than in those studies where online learners worked independently (Means et al., 2010 ).

On the other hand, evidence by Brown and Liedholm ( 2002 ) based on test scores from macroeconomics students in the United States suggest that F2F students tend to outperform online students. These findings are supported by Coates et al. ( 2004 ) who base their study on macroeconomics students in the United States, and Xu and Jaggars ( 2014 ) who find negative effects for online students using a data set of about 500,000 courses taken by over 40,000 students in Washington. Furthermore, Almatra et al. ( 2015 ) compare overall course grades between online and F2F students for a Telecommunications course and find that F2F students significantly outperform online learning students. In an experimental study where students are randomly assigned to attend live lectures versus watching the same lectures online, Figlio et al. ( 2013 ) observe some evidence that the traditional format has a positive effect compared to online format. Interestingly, Callister and Love ( 2016 ) specifically compare the learning outcomes of online versus F2F skills‐based courses and find that F2F learners earned better outcomes than online learners even when using the same technology. This study highlights that some of the inconsistencies that we find in the results comparing online to F2F learning might be influenced by the nature of the course: theory‐based courses might be less impacted by in‐person interaction than skills‐based courses.

The fact that the reviewed studies on the effects of F2F versus online learning on student performance have been mainly focused in developed countries indicates the dearth of similar studies being conducted in developing countries. This gap in the literature may also highlight a salient point: online learning is still relatively underexplored in developing countries. The lockdown in South Africa therefore provides us with an opportunity to contribute to the existing literature from a developing country context.

2. CONTEXT OF STUDY

South Africa went into national lockdown in March 2020 due to the Covid‐19 pandemic. Like most universities in the country, the first semester for undergraduate courses at the University of Pretoria had already been running since the start of the academic year in February. Before the pandemic, a number of F2F lectures and assessments had already been conducted in most courses. The nationwide lockdown forced the university, which was mainly in‐person teaching, to move to full online learning for the remainder of the semester. This forced shift from F2F teaching to online learning allows us to investigate the changes in students' performance.

Before lockdown, classes were conducted on campus. During lockdown, these live classes were moved to an online platform, Blackboard Collaborate, which could be accessed by all registered students on the university intranet (“ClickUP”). However, these live online lectures involve substantial internet data costs for students. To ensure access to course content for those students who were unable to attend the live online lectures due to poor internet connections or internet data costs, several options for accessing course content were made available. These options included prerecorded narrated slides (which required less usage of internet data), recordings of the live online lectures, PowerPoint slides with explanatory notes and standard PDF lecture slides.

At the same time, the university managed to procure and loan out laptops to a number of disadvantaged students, and negotiated with major mobile internet data providers in the country for students to have free access to study material through the university's “connect” website (also referred to as the zero‐rated website). However, this free access excluded some video content and live online lectures (see Table 1 ). The university also provided between 10 and 20 gigabytes of mobile internet data per month, depending on the network provider, sent to students' mobile phones to assist with internet data costs.

Sites available on zero‐rated website

Note : The table summarizes the sites that were available on the zero‐rated website and those that incurred data costs.

High data costs continue to be a contentious issue in Africa where average incomes are low. Gilbert ( 2019 ) reports that South Africa ranked 16th of the 45 countries researched in terms of the most expensive internet data in Africa, at US$6.81 per gigabyte, in comparison to other Southern African countries such as Mozambique (US$1.97), Zambia (US$2.70), and Lesotho (US$4.09). Internet data prices have also been called into question in South Africa after the Competition Commission published a report from its Data Services Market Inquiry calling the country's internet data pricing “excessive” (Gilbert, 2019 ).

3. EMPIRICAL APPROACH

We use a sample of 395 s‐year students taking a macroeconomics module in the Economics department to compare the effects of F2F and online learning on students' performance using a range of assessments. The module was an introduction to the application of theoretical economic concepts. The content was both theory‐based (developing economic growth models using concepts and equations) and skill‐based (application involving the collection of data from online data sources and analyzing the data using statistical software). Both individual and group assignments formed part of the assessments. Before the end of the semester, during lockdown in June 2020, we asked the students to complete a survey with questions related to the transition from F2F to online learning and the difficulties that they may have faced. For example, we asked the students: (i) how easy or difficult they found the transition from F2F to online lectures; (ii) what internet options were available to them and which they used the most to access the online prescribed work; (iii) what format of content they accessed and which they preferred the most (i.e. self‐study material in the form of PDF and PowerPoint slides with notes vs. assisted study with narrated slides and lecture recordings); (iv) what difficulties they faced accessing the live online lectures, to name a few. Figure 1 summarizes the key survey questions that we asked the students regarding their transition from F2F to online learning.

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Object name is AFDR-33-S114-g002.jpg

Summary of survey data

Before the lockdown, the students had already attended several F2F classes and completed three assessments. We are therefore able to create a dependent variable that is comprised of the average grades of three assignments taken before lockdown and the average grades of three assignments taken after the start of the lockdown for each student. Specifically, we use the difference between the post‐ and pre‐lockdown average grades as the dependent variable. However, the number of student observations dropped to 275 due to some students missing one or more of the assessments. The lecturer, content and format of the assessments remain similar across the module. We estimate the following equation using ordinary least squares (OLS) with robust standard errors:

where Y i is the student's performance measured by the difference between the post and pre‐lockdown average grades. B represents the vector of determinants that measure the difficulty faced by students to transition from F2F to online learning. This vector includes access to the internet, study material preferred, quality of the online live lecture sessions and pre‐lockdown class attendance. X is the vector of student demographic controls such as race, gender and an indicator if the student's perceived family income is below average. The ε i is unobserved student characteristics.

4. ANALYSIS

4.1. descriptive statistics.

Table 2 gives an overview of the sample of students. We find that among the black students, a higher proportion of students reported finding the transition to online learning more difficult. On the other hand, more white students reported finding the transition moderately easy, as did the other races. According to Coetzee ( 2014 ), the quality of schools can vary significantly between higher income and lower‐income areas, with black South Africans far more likely to live in lower‐income areas with lower quality schools than white South Africans. As such, these differences in quality of education from secondary schooling can persist at tertiary level. Furthermore, persistent income inequality between races in South Africa likely means that many poorer black students might not be able to afford wifi connections or large internet data bundles which can make the transition difficult for black students compared to their white counterparts.

Descriptive statistics

Notes : The transition difficulty variable was ordered 1: Very Easy; 2: Moderately Easy; 3: Difficult; and 4: Impossible. Since we have few responses to the extremes, we combined Very Easy and Moderately as well as Difficult and Impossible to make the table easier to read. The table with a full breakdown is available upon request.

A higher proportion of students reported that wifi access made the transition to online learning moderately easy. However, relatively more students reported that mobile internet data and accessing the zero‐rated website made the transition difficult. Surprisingly, not many students made use of the zero‐rated website which was freely available. Figure 2 shows that students who reported difficulty transitioning to online learning did not perform as well in online learning versus F2F when compared to those that found it less difficult to transition.

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Object name is AFDR-33-S114-g003.jpg

Transition from F2F to online learning.

Notes : This graph shows the students' responses to the question “How easy did you find the transition from face‐to‐face lectures to online lectures?” in relation to the outcome variable for performance

In Figure 3 , the kernel density shows that students who had access to wifi performed better than those who used mobile internet data or the zero‐rated data.

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Object name is AFDR-33-S114-g001.jpg

Access to online learning.

Notes : This graph shows the students' responses to the question “What do you currently use the most to access most of your prescribed work?” in relation to the outcome variable for performance

The regression results are reported in Table 3 . We find that the change in students' performance from F2F to online is negatively associated with the difficulty they faced in transitioning from F2F to online learning. According to student survey responses, factors contributing to difficulty in transitioning included poor internet access, high internet data costs and lack of equipment such as laptops or tablets to access the study materials on the university website. Students who had access to wifi (i.e. fixed wireless broadband, Asymmetric Digital Subscriber Line (ADSL) or optic fiber) performed significantly better, with on average 4.5 points higher grade, in relation to students that had to use mobile internet data (i.e. personal mobile internet data, wifi at home using mobile internet data, or hotspot using mobile internet data) or the zero‐rated website to access the study materials. The insignificant results for the zero‐rated website are surprising given that the website was freely available and did not incur any internet data costs. However, most students in this sample complained that the internet connection on the zero‐rated website was slow, especially in uploading assignments. They also complained about being disconnected when they were in the middle of an assessment. This may have discouraged some students from making use of the zero‐rated website.

Results: Predictors for student performance using the difference on average assessment grades between pre‐ and post‐lockdown

Coefficients reported. Robust standard errors in parentheses.

∗∗∗ p < .01.

Students who expressed a preference for self‐study approaches (i.e. reading PDF slides or PowerPoint slides with explanatory notes) did not perform as well, on average, as students who preferred assisted study (i.e. listening to recorded narrated slides or lecture recordings). This result is in line with Means et al. ( 2010 ), where student performance was better for online learning that was collaborative or instructor‐driven than in cases where online learners worked independently. Interestingly, we also observe that the performance of students who often attended in‐person classes before the lockdown decreased. Perhaps these students found the F2F lectures particularly helpful in mastering the course material. From the survey responses, we find that a significant proportion of the students (about 70%) preferred F2F to online lectures. This preference for F2F lectures may also be linked to the factors contributing to the difficulty some students faced in transitioning to online learning.

We find that the performance of low‐income students decreased post‐lockdown, which highlights another potential challenge to transitioning to online learning. The picture and sound quality of the live online lectures also contributed to lower performance. Although this result is not statistically significant, it is worth noting as the implications are linked to the quality of infrastructure currently available for students to access online learning. We find no significant effects of race on changes in students' performance, though males appeared to struggle more with the shift to online teaching than females.

For the robustness check in Table 4 , we consider the average grades of the three assignments taken after the start of the lockdown as a dependent variable (i.e. the post‐lockdown average grades for each student). We then include the pre‐lockdown average grades as an explanatory variable. The findings and overall conclusions in Table 4 are consistent with the previous results.

Robustness check: Predictors for student performance using the average assessment grades for post‐lockdown

As a further robustness check in Table 5 , we create a panel for each student across the six assignment grades so we can control for individual heterogeneity. We create a post‐lockdown binary variable that takes the value of 1 for the lockdown period and 0 otherwise. We interact the post‐lockdown dummy variable with a measure for transition difficulty and internet access. The internet access variable is an indicator variable for mobile internet data, wifi, or zero‐rated access to class materials. The variable wifi is a binary variable taking the value of 1 if the student has access to wifi and 0 otherwise. The zero‐rated variable is a binary variable taking the value of 1 if the student used the university's free portal access and 0 otherwise. We also include assignment and student fixed effects. The results in Table 5 remain consistent with our previous findings that students who had wifi access performed significantly better than their peers.

Interaction model

Notes : Coefficients reported. Robust standard errors in parentheses. The dependent variable is the assessment grades for each student on each assignment. The number of observations include the pre‐post number of assessments multiplied by the number of students.

6. CONCLUSION

The Covid‐19 pandemic left many education institutions with no option but to transition to online learning. The University of Pretoria was no exception. We examine the effect of transitioning to online learning on the academic performance of second‐year economic students. We use assessment results from F2F lectures before lockdown, and online lectures post lockdown for the same group of students, together with responses from survey questions. We find that the main contributor to lower academic performance in the online setting was poor internet access, which made transitioning to online learning more difficult. In addition, opting to self‐study (read notes instead of joining online classes and/or watching recordings) did not help the students in their performance.

The implications of the results highlight the need for improved quality of internet infrastructure with affordable internet data pricing. Despite the university's best efforts not to leave any student behind with the zero‐rated website and free monthly internet data, the inequality dynamics in the country are such that invariably some students were negatively affected by this transition, not because the student was struggling academically, but because of inaccessibility of internet (wifi). While the zero‐rated website is a good collaborative initiative between universities and network providers, the infrastructure is not sufficient to accommodate mass students accessing it simultaneously.

This study's findings may highlight some shortcomings in the academic sector that need to be addressed by both the public and private sectors. There is potential for an increase in the digital divide gap resulting from the inequitable distribution of digital infrastructure. This may lead to reinforcement of current inequalities in accessing higher education in the long term. To prepare the country for online learning, some considerations might need to be made to make internet data tariffs more affordable and internet accessible to all. We hope that this study's findings will provide a platform (or will at least start the conversation for taking remedial action) for policy engagements in this regard.

We are aware of some limitations presented by our study. The sample we have at hand makes it difficult to extrapolate our findings to either all students at the University of Pretoria or other higher education students in South Africa. Despite this limitation, our findings highlight the negative effect of the digital divide on students' educational outcomes in the country. The transition to online learning and the high internet data costs in South Africa can also have adverse learning outcomes for low‐income students. With higher education institutions, such as the University of Pretoria, integrating online teaching to overcome the effect of the Covid‐19 pandemic, access to stable internet is vital for students' academic success.

It is also important to note that the data we have at hand does not allow us to isolate wifi's causal effect on students' performance post‐lockdown due to two main reasons. First, wifi access is not randomly assigned; for instance, there is a high chance that students with better‐off family backgrounds might have better access to wifi and other supplementary infrastructure than their poor counterparts. Second, due to the university's data access policy and consent, we could not merge the data at hand with the student's previous year's performance. Therefore, future research might involve examining the importance of these elements to document the causal impact of access to wifi on students' educational outcomes in the country.

ACKNOWLEDGMENT

The authors acknowledge the helpful comments received from the editor, the anonymous reviewers, and Elizabeth Asiedu.

Chisadza, C. , Clance, M. , Mthembu, T. , Nicholls, N. , & Yitbarek, E. (2021). Online and face‐to‐face learning: Evidence from students’ performance during the Covid‐19 pandemic . Afr Dev Rev , 33 , S114–S125. 10.1111/afdr.12520 [ CrossRef ] [ Google Scholar ]

1 https://mybroadband.co.za/news/cellular/309693-mobile-data-prices-south-africa-vs-the-world.html .

2 The 4IR is currently characterized by increased use of new technologies, such as advanced wireless technologies, artificial intelligence, cloud computing, robotics, among others. This era has also facilitated the use of different online learning platforms ( https://www.brookings.edu/research/the-fourth-industrialrevolution-and-digitization-will-transform-africa-into-a-global-powerhouse/ ).

3 Note that we control for income, but it is plausible to assume other unobservable factors such as parental preference and parenting style might also affect access to the internet of students.

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Research article
Open access
Published: 02 December 2020

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 ).

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Shaping the Future of Online Learning

Published may 22, 2024.

If you’ve been enrolled in any educational course or postsecondary educational program since 2020, chances are you’ve witnessed the rise in online learning firsthand .

The COVID-19 global pandemic shuttered storefronts, theaters, and classrooms alike, causing major disruptions in how goods and services were delivered. As consumers adopted Instacart for their grocery needs and streamed new blockbuster movies from the comfort of their living rooms, students needed an innovative way to bring their classes home. A year into the pandemic over 60% of all undergraduate students were enrolled in at least one online course , with 28% exclusively enrolled in online courses, according to the National Center for Education Statistics.

There are other reasons for the widespread adoption, including accessibility. Rural and international students who may be far removed from traditional educational institutions can now attend Harvard classes anywhere there’s an internet connection. Or, consider working adults seeking to progress or switch careers. Life doesn’t stop for a class, and attending one in-person can be prohibitive. While still challenging, logging into a virtual classroom is far more manageable. Online education is for everyone.

Technological and pedagogical developments have helped online learning progress beyond the days of discussion boards and essay uploads. Now, students can enjoy a multimedia educational experience that is rooted in the latest research, all while participating in the community of their “virtual campus”.

If you’re one of the millions of learners who have experienced online education, you might be interested to learn where it’s going next. At Harvard Online, the question, “what is the future of online learning?” guides an ongoing conversation that drives us everyday.

In this blog, we sat down with Catherine Breen , Managing Director of Harvard Online. With more than two decades of senior executive leadership at Harvard University and oversight of Harvard Online, Breen has an invaluable perspective on the future of online learning, and the exciting role Harvard Online is playing in bringing the future into the present.

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Catherine Breen, Managing Director of Harvard Online, in a team meeting.

Harvard Online (HO): How has the online learning landscape evolved in recent years?

Catherine Breen (CB): At the beginning of the COVID-19 lockdown, there was a massive escalation in demand for online learning. Demand began to recede slowly as the months wore on and by late 2022, it started to level out. But we observed two big changes: Internally, the demand for Harvard Online content was still almost three times higher than pre-pandemic. Externally, in reaction to the demand surge, there was significant and rapid growth of new online course offerings and companies that purveyed varying types of digital products.

HO: What is shaping the future of online learning today?

CB: Because of the rapid and massive shift to online that occurred around the globe in the spring of 2020, the landscape changed permanently. There are many things shaping the future but here are just a few that I can see from my perspective:

Increased adoption of online learning across all ages and levels of education: Everyone expanded their online course catalogs; new companies and offerings sprung up everywhere.
Greater tech investment across organizations and industries: Organizations are investing more time, money, and effort into technology infrastructure, tools, and platforms to support online learning and participants in these courses.
New pedagogical methods to bridge the gap between traditional and novel learning methods: Instructors have adapted their teaching methods for online, hybrid, and blended environments.
Enhanced accessibility to quality education and learning experiences: Efforts have been made to improve access for students of all types, abilities, geographies, and backgrounds so that everyone can participate effectively.

HO: What are the remaining challenges that online learning faces?

CB: While these changes have improved the online learning experience, challenges remain, including addressing the digital divide, maximizing student engagement, and refining the quality of online courses.

The pandemic accelerated the adoption of online learning and its impact will likely continue to shape higher education for many years to come.

HO: How does online learning contribute to Harvard's mission of promoting accessibility and inclusion in education, especially for learners who may not have traditional access to higher education?

CB: Online learning levels the playing field for learners in many ways.

Most students think that a Harvard-quality education is out of reach, for a variety of reasons. With online courses, however, learners from around the country and the world can take courses with Harvard instructors at their own pace at a more affordable price point.

Our online courses also typically incorporate a range of multimedia elements, allowing students with different learning styles to flourish. We also ensure that our online learning experiences are accessible to all learners, including those with disabilities. This commitment to inclusivity aligns with the broader goals of promoting equitable access to education.

Lastly, our online courses often include discussion forums and virtual communities where learners can connect and collaborate. This allows for interactions among students from diverse backgrounds and experiences, fostering a sense of belonging and inclusion.

It’s clear that online learning has a lot to offer everyone, and it’s only getting better. In our next blog in this series, we’ll hear more from Cathy on how institutions can implement online learning modalities effectively.

If you missed the first blog in this series detailing the future of online learning, you can check out the first blog here . To learn more about Harvard Online, explore our fully online course catalog here .

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Analysis of the Writing Quality of Senior High School Students’ Research Introductions

Research introduction as a genre has been a fertile subject of inquiry in recent years. However, much of the research has focused primarily on the analysis of its generic structure, paying less attention to its other equally important elements . Hence, examining the quality of research introductions using multiple criteria, particularly among novice research writers, is pivotal in the field of second language (L2) academic writing in a secondary education context. This study was conducted to examine the writing quality of research introductions among senior high school (SHS) students and to further probe whether a significant difference existed in the students’ writing quality when grouped according to their chosen educational track. The data involved 60 research introductions collaboratively written and submitted as preliminary examination papers by SHS students in an online research writing course. Using a modified rubric from Tuyen et al. (2018), the research introductions were rated in terms of their content, organization, language use, mechanics, and citation, and were subjected to descriptive and inferential analyses. Triangulation using qualitative evidence was undertaken to illustrate the students’ actual writing quality. The results show that the overall writing quality of the students’ research introductions was poor, and there was a significant difference in their writing quality when they were grouped according to educational track. The results indicate a need for a more holistic theoretical framework on academic writing and a call for a genre-oriented approach to teaching academic writing in a secondary education context.

https://doi.org/10.26803/ijlter.23.5.12

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