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The Value of Face-to-Face Classes: An Argumentative Exploration of In-Person Learning

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• Hattie, J. A. C., & Donoghue, G. M. (2016). Learning strategies: A synthesis and conceptual model. npj Science of Learning, 1(1), 16013.
• Pascarella, E. T., & Terenzini, P. T. (2005). How college affects students: A third decade of research (Vol. 2). Jossey-Bass.

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Home — Essay Samples — Education — Online Vs. Traditional Classes — Online Learning vs Face-to-Face

Online Learning Vs Face-to-face

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Published: Aug 24, 2023

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Advantages of online learning, disadvantages of online learning, advantages of face-to-face education, disadvantages of face-to-face education.

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• Published: 18 August 2023

Exploring student perceptions and use of face-to-face classes, technology-enhanced active learning, and online resources

• Joanne M. Lewohl   ORCID: orcid.org/0000-0002-7577-0734 1  

International Journal of Educational Technology in Higher Education volume  20 , Article number:  48 ( 2023 ) Cite this article

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The current cohort of undergraduate students is often said to value technology and is assumed to prefer immersive, interactive, and personalized learning experiences. In contrast, many educators recognise the value of face-to-face classes and believe that attending class positively impacts student performance. A novel teaching strategy, including traditional lectures and interactive workshops using an educational technology platform were implemented in an undergraduate neurobiology course. Attendance in class and use of lecture capture recording were associated with improved student performance. Further, student attitudes toward the teaching strategy were evaluated via a survey. The survey respondents included those that regularly attended class and those that did not. Overall, irrespective of attendance, students thought that face-to-face classes were beneficial to their learning and the use of active learning activities helped them to understand the course content. The most common reasons for non-attendance in class were attributed to factors such as the class schedule, work and family commitments and were not related to the availability of class recordings and other online resources. In contrast, the most common reasons for attendance in class included the perceived benefit, the standard of teaching and the level of interest in the course. The novel teaching strategy had a positive impact on student learning, and can be used for in-person, online and asynchronous learning, providing a mechanism for educators to cater for students who wish to attend in-person classes as well as providing options for flexible delivery.

Graphical Abstract

Introduction

Each new generation of students has characteristics, interests and learning preferences that set them apart from the previous generation, and understanding these differences is necessary for educators to create learning environments that are engaging, inspiring and productive (Poláková & Klímová, 2019 ). The current cohort of undergraduate students are often described as individuals who have grown up with technology as an integral part of their daily lives (Seemiller & Grace, 2016 ). They are thought to be highly adaptable to new technology and expect their learning experiences to be immersive, interactive, and personalized (Reviewed in (Shorey et al., 2021 )). This cohort of students are also considered to be more independent learners, often relying on online resources to support their education, with a preference for and the ability to learn at their own pace (Chicca & Shellenbarger, 2018 ; Seemiller & Grace, 2016 ).

In 2020, the global coronavirus pandemic necessitated a rapid pivot to online and blended learning at universities in Australia and around the world, accelerating the trends that were already in process (Watermeyer et al., 2021 ). As a result, there has been a rapid expansion into the online learning space and an increasing reliance on the use of educational technology and virtual learning environments to deliver content and to facilitate online learning (Reviewed in (Arday, 2022 )). As educators, we are entering an unprecedented era, one in which we are tasked with providing high quality instruction to engage students in their own learning despite the potential for ongoing educational disruption. There are many challenges in this changing landscape including how to cater to students who want the flexibility of studying online or asynchronously with those that want to return to face-to-face delivery.

Prior to the pandemic, a common mode of instruction at university was the traditional didactic lecture, although technology-enhanced active learning, problem-based learning and flipped classroom strategies have also become popular (Kirkwood & Price, 2014 ). Educators often placed value on in-class attendance which was viewed as an important indicator of student success (Crede et al., 2010 ; Guleker & Keci, 2014 ). Indeed, a systematic review of the relationship between lecture attendance and academic achievement revealed that 75% of studies showed a significant positive association between class attendance and academic performance for undergraduate students in the biosciences (Doggrell, 2020b ). However, there is an increasing trend at our Institution and others to provide lecture capture recordings and to develop online digital resources to facilitate student learning. The provision of these resources offers increased flexibility for students to engage with the course content, but a common concern is that this may negatively affect attendance and may not improve student outcomes (Gosper et al., 2010 ; Kinash et al., 2015 ; Preston et al., 2010 ). Specifically, the availability of captured lectures has been postulated to reduce student interaction in face-to-face classes (Mark et al., 2010 ). Attendance rates for students vary widely and the reasons for absenteeism often include student perception of the value of traditional lectures as well as the availability of class recordings and other online resources (Reviewed in (James & Seary, 2019 )). There is also the potential for traditional modes of delivery to be at odds with the learning preferences of the current generation of students (Shorey et al., 2021 ).

Technology-enhanced learning is a broad term that can be used to describe any form of e-learning. Accordingly, technology-enhanced learning strategies can refer the use of technology to improve learning in face-to-face classes, the creation and use of digital resources for asynchronous learning or using social media (and other platforms) to encourage collaborative learning (Ansari & Khan, 2020 ; Voorn & Kommers, 2013 ). The impact of these strategies on student learning is reliant on the student’s engagement with and usage of the specific technological platform that is implemented (Dunn & Kennedy, 2019 ). While the impact of in-class attendance on academic achievement has been extensively studied (Crede et al., 2010 ; Guleker & Keci, 2014 ), when technology-enhanced learning strategies are implemented, the relationship between student attendance and academic performance is more difficult to ascertain. Some studies have shown no correlation between class attendance and performance in courses where lectures are recorded and class materials are available online (Doggrell, 2020a ; Kauffman et al., 2018 ). Other studies have shown that students who study independently, using online resources, can have similar academic outcomes and may even outperform those who attend class (Eisen et al., 2015 ; Lukkarinen et al., 2016 ).

Active learning is a key component to undergraduate science, technology, engineering, and mathematics (STEM) education (Freeman et al., 2014 ) however, lectures at higher degree institutions are often held in learning spaces that are not conducive to in-class participation (Büchele, 2021 ; Fadelelmoula, 2018 ; O'Keeffe et al., 2017 ). To overcome this challenge, educators often use technology to enhance the learning experiences for students (Wood et al., 2018 ). Echo360 is a platform that is commonly used for the automatic recording of classes. The newest iteration of this product, the Echo360 Active Learning Platform (Echo360ALP), is a technology-enhanced learning platform designed to facilitate active learning, promoting student engagement and participation (Shaw et al., 2015 ). The Echo360ALP has been available at Griffith University from 2018. Its functionality includes the ability for educators to embed polling questions at strategic points in their presentations and students can log in and answer these questions in real time. This active learning platform also includes the ability to directly embed multimedia into in-class presentations which is likely to appeal to learners who prefer to seek information through visual learning (Seemiller & Grace, 2016 ). Using a technology-enhanced active learning platform as a tool, it is possible to create novel and innovative learning experiences which may encourage students to attend class and engage with class material.

The present study

The learning preferences of the current cohort of students for immersive, interactive, and flexible learning experiences are at odds with the traditional didactic delivery of lectures at university. To address this issue, a novel teaching strategy was implemented in a second-year undergraduate neurobiology course incorporating a unique blend of traditional lectures, active and interactive learning strategies, and online learning resources. Specifically, face-to-face classes included traditional didactic lectures which were used to deliver course content, and workshop classes that used an active learning platform to facilitate student interaction and engagement during class (Freeman et al., 2014 ; Shaw et al., 2015 ). In addition, all classes, were recorded and made available to students asynchronously. The teaching strategy was designed to meet the diverse needs of students and was aimed at fostering student engagement and motivation to attend class and engage with the course materials (Dunn & Kennedy, 2019 ). Thus, a key objective of the current study was to investigate student attendance in face-to-face classes, their use of class recordings, and the impact of these on student performance in the course. Students were also surveyed to establish their views on the teaching strategy including the use of an active learning platform in the classroom, their use of the available resources as well as factors that influenced their decision to attend, or not attend classes in person.

Cohort characteristics

The study participants were second-year undergraduate neurobiology students who completed the course as part of their program of study at Griffith University. Ethical Clearance for this project was obtained from the Griffith University Human Ethics Committee (GU Ref No: 2018/651). The course is offered in one 12-week trimester each year with two distinct cohorts analysed in this study (2018 and 2019). The course is a requirement for students in the Bachelor of Biomedical Science program and an elective for students in other health programs. Many of the students in these programs have career trajectories that include medicine, medical research, or allied health professions. In 2018 the cohort consisted of 115 students; 85 (74%) were from the Biomedical Science program, and 21 (18.3%) were from the Health Science program. The remaining nine students were from other health-related programs. In 2019 the cohort consisted of 93 students; 63 (72%) were from the Biomedical Science program, 25 (26.9%) were from the Health Science program, and the remaining student was from another health-related program. In the 2018 cohort there were 67 female students (58.3%) and 48 male students (41.7%). In the 2019 cohort there were 54 female students (58.1%) and 39 male students (41.9%). Data from both cohorts were combined in the analysis.

Educational context and course structure

The course was designed using a constructivist approach (Biggs, 2014 ) and consists of a series of scaffolded weekly topics starting with fundamental topics (e.g. neuroanatomy) and progressing to more complex integrated topics (e.g. dementia). The main objective of the course is to teach students about the function of the brain and specifically how damage to discrete areas of the brain results in the symptoms associated with various neurological and neuropathological conditions.

Each topic was designed and structured using Bloom’s Taxonomy (Krathwohl, 2002 ). The theoretical content was taught in face-to-face lectures which were automatically recorded using the Echo360 lecture capture system and made available within a few hours of the scheduled class. Additional digital resources including detailed learning objectives, presentation slides, and review questions were available for each topic. Students also had access to an online interactive textbook hosted by a third-party vendor that was authored by the course instructor, free to access under specific conditions and directly aligned to the course outcomes. The online textbook included formative assessments in the form of quiz questions as well as embedded multimedia usually in the form of YouTube videos that were vetted for appropriateness and accuracy of content.

To encourage student engagement and facilitate deep learning, each topic also included clinical case studies to provide a real-world context for students (Meil, 2007 ; Mickley & Hoyt, 2010 ). Students were expected to engage with and acquire knowledge about each topic from one of the available resources (in-person lecture, recorded lecture, or interactive textbook), and then apply that knowledge to analyse case studies in the workshop classes. For some topics, the theory and applied components were combined in a single class. The workshops (14 in total) were designed to be interactive and used an active learning platform. Each workshop included at least one case study and included polling questions which the students could answer in real-time as well as multimedia (video) which was used to showcase patient symptoms. The workshop classes were also automatically recorded using the lecture capture system. Students had access to the recording itself as well as the presentation files which included the embedded polling questions.

The timetable, timing and form of assessment, venue and teaching staff were consistent for the course offering in both 2018 and 2019. All lectures and interactive workshop classes were delivered by a single instructor in both 2018 and 2019.

Student attainment measures

The final exam was worth 50% of the overall grade and was held during the exam period at the end of the trimester. This item of assessment was conducted in-person, under exam conditions and included case study questions like those presented in interactive workshop classes. The dependent measures used as measures of student performance were the final exam percentage ( Final Exam ) and final overall percentage ( Overall Percentage ). Student grades for two pre-requisite anatomy and physiology courses were available for most students (83.5% of students in 2018 and 92.5% in 2019). The average pre-requisite grade was determined for each student individually and was used as a variable in the bivariate Pearson’s correlation analysis. Students without grades for the first-year courses were external transfers who received credit for the course.

Class attendance & in-class participation

Attendance was recorded in 14 classes during the trimester. A list of student names was circulated during the class. Students could sign in on entry to the class or mark their name off as the clipboard circulated through the room. The sign-in sheets were also available during the 10-min break in the middle of the two-hour class and at the end of class for any student who had not marked off their name. Attendance, as expressed as a percentage of enrolled students, was determined for each individual class ( Class Attendance ). Student Attendance was calculated as the total number of classes attended by each individual student (0–14). In-class participation was defined as the number of students who logged in to the active learning platform during class expressed as a percentage of the number of students who attended in-person.

Lecture capture analytics

Lecture capture data was downloaded once for each cohort on the day of the final exam and therefore reflects the number of views during the trimester and in the review period on the lead-up to the final exam. For each individual student, the viewing data was extracted for each class recording and included the view duration, capture duration and percentage of video viewed. If a student accessed and watched more than 10% of a recording it was counted as a “ View ”. If a student accessed and watched more than 80% of the recording it was counted as a “ Complete View ”. If a student accessed and watched between 10 and 80% of the recording it was counted as a “ Partial View ”.

Data analysis

The data was analysed using IBM SPSS Statistics 28 software (SPSS Inc. Chicago, IL, USA). The relationship between Student Attendance (total number of classes attended; 0 – 14), Complete Views (number of class recordings where > 80% of the recording was watched by the student; 0 – 14) and Partial Views (number of class recordings where 10 – 80% of the recording was watched by the student; 0–14) and performance in both the final exam and the course overall was investigated using bivariate Pearson’s correlation analysis. To determine if performance in the pre-requisite courses influenced the relationship between these variables, a partial correlation analysis was performed. Analysis of Variance (ANOVA) and post hoc testing (Tukey HSD) was used to determine if differences in student performance measures reached statistical significance (using an α of 0.05).

Student attitudinal survey and data analysis

Data was collected by means of an attitudinal survey. The survey was adapted from previous studies assessing student perspectives to lecture attendance in undergraduate engineering (Fitzpatrick et al., 2011 ) and neuroscience courses (O'Keeffe et al., 2017 ). In 2018, the survey was administered in person in the final class of the year. In 2019, the survey was administered online. The survey included questions regarding demographic information, questions about their attendance in each type of class, their opinions about face-to-face classes and lecture capture as well as questions about the resources provided in the learning environment (Table 1 ). Students were also asked to indicate their reasons for attendance or non-attendance in face-to-face classes by completing a matrix of possible predetermined options. Students who identified as “ non-attenders ” were given a choice of 17 options and asked to indicate whether it was “never a reason”, “sometimes a reason” or “definitely a reason” for their non-attendance (Table 2 ). Students who identified as “ attenders ” were asked to respond to 14 options with the same three possible responses (Table 3 ). The survey also included three open questions designed to solicit opinions about attending class and active learning strategies.

The data from each survey was exported to Excel and responses to each question were counted to determine the percentage of students with each response. For questions regarding student opinion of lectures and workshops, data was collected using a 5-point Likert scale. The 5-point Likert scale consisted of the following options: “strongly disagree”, “disagree”, “undecided”, “agree” and “strongly agree”. The responses were converted to ordinal data ranging from 1 to 5 with 1 = “strongly disagree” to 5 = “strongly agree”. A positive response was indicated by selection of either the “strongly agree” or “agree” option, a negative response was indicated by selection of either the “strongly disagree” or disagree” option. The final option was “undecided” indicating no clear agreement or disagreement with the statement.

The data was analysed using IBM SPSS Statistics 25 software (SPSS Inc. Chicago, IL, USA). For each statement, descriptive statistics including the mean score were calculated. Further, Pearson’s Chi-squared tests were performed to determine whether the student’s choice significantly deviated from chance where the expected outcome was defined as equal numbers of students selecting each option. Students were also asked to indicate their reasons for attendance or non-attendance in face-to-face classes by completing a matrix of possible (predetermined) options. However, students were able to contribute additional responses and reasons via open ended questions.

What resources did the students use?

Attendance in lectures and workshops was not mandatory and students were able to choose whether to attend class in person, use the class recordings as a substitute or a combination of both according to their own preferences. The class materials, including the class recordings and online interactive textbook, were available to all students enrolled in the course and the variety and comprehensiveness of the resources allowed students the flexibility to study independently if they chose.

Workshop attendance varied from 25.8 to 73.1% throughout the trimester for an average of 46%. Individual student attendance ranged from 0 to 100%. While students were encouraged to bring a laptop or other mobile device for in-class polling activities using the active learning platform it was not mandatory. In both cohorts there was a mixture of students who logged in and those who did not. The average percentage of students who logged in to the active learning platform during class was 59.6% (range: 17.8–86.1%).

Lecture capture usage varied across classes, with the average number of views per recording ranging from 99 to 263 (average: 152 views/class). The percentage of students viewing the recorded lectures ranged from 38 to 74% (average: 53.5%). Further, the percentage of students watching more than 80% of the recording ranged from 22 to 60% (average: 56%). Of the 205 analyzed students, 14 attended class in-person but did not watch the recordings (“ Attenders ”), 26 watched more than 80% of each class recording but did not attend in-person (“ Viewers ”), 29 attended class and watched more than 80% of each recording (“ High Engagers ”) and 15 neither attended class in person nor watched the recordings (“ Low Engagers ”).

The online textbook, hosted by a third-party vendor, was accessible at no cost to students under specific circumstances. Approximately 45% of students in the cohort signed up to access the online textbook but since it was hosted externally, precise tracking data was not available.

How did the students perform in the course?

In terms of in-person attendance, a weak but significant positive relationship was found between Student Attendance and performance on the final exam (R 205  = 0.284, P < 0.001) and in the overall course percentage (R 205  = 0.268, P < 0.001). These relationships remained significant even after controlling for average pre-requisite grade (Final Exam Percentage: R 185  = 0.258, P < 0.001; Final Overall Percentage: R 205  = 0.235, P = 0.001).

Regarding the impact of watching class recordings, a weak but significant positive relationship was found between watching more than 80% of each recording ( Complete Views ) and performance on both the final exam ( R 203  = 0.29, P  < 0.001) and in the overall course percentage ( R 203  = 0.307, P  < 0.001). These relationships remained significant even when controlling for average pre-requisite grade (Final Exam: R 182  = 0.279, P  < 0.001; Final Overall Percentage: R 183  = 0.316, P  = 0.001). However, no significant relationship was found between partial lecture capture views ( Partial Views ) and performance on the final exam ( R 203  = 0.02, P  = 0.774) or in the overall course percentage ( R 205  = 0.004, P  = 0.955).

To determine if watching more than 80% of each class recordings is equivalent to attending class in person, the performance of “ Attenders ” was compared with that of “ Viewers ”. These two groups of students performed similarly in both the final exam (Tukey HSD; P  = 0.965) and overall course percentage ( P  = 0.975) suggesting that watching the class recordings can serve as an adequate substitute for attending in person. Further, both “ Attenders ” and “ Viewers ” outperformed “ Low Engagers ” on the final exam (“ Attenders ” vs “ Low Engagers ”, P  = 0.004; “ Viewers ” vs “ Low Engagers ”, P  = 0.032) and in the course overall (“ Attenders ” vs “ Low Engagers ”, P  = 0.001; “ Viewers ” vs “ Low Engagers ”, P  = 0.009). “ High Engagers ” performed at a similar level to “ Attenders ” (Final Exam, P  = 0.899; Overall Percentage, P  = 0.975) and “ Viewers ” (Final Exam, P  = 1.00; Overall Percentage, P  = 1.00) on both the final exam and in the course overall.

Student perspectives on the relevance of face-to-face classes

In total, 105 students completed surveys: 68 students in 2018 (59.1%) and 37 students in 2019 (40.2%). Overall, 78.1% of the students were 15–20 years of age and a further 20% of students were 21–30 years of age. There was one student who as 31–40 years of age and one who was in the 41–50-year age bracket. There were more females (66.67%) than males (33.33%). The majority (86.67%) of students used English as their first language and 93.33% of the cohort were domestic students. Most of the respondents were students in the Bachelor of Biomedical Science program (76.2%) and a further 17.14% were in the Bachelor of Health Science program. The remaining students were enrolled in a variety of other programs in the Faculty of Health. Of the students who completed the survey, 62.9% attended more than 50% of lectures, 17.1% attended less than 50% of lectures and 20% did not attend any lectures. Of the students who completed the survey, 72.4% attended more than 50% of workshops, 19% attended less than 50% of workshops and 8.6% did not attend any workshops.

Students were asked to indicate their level of agreement with five statements related to their experience of the course (see Table 1 for details). Out of 105 surveyed students, the majority found lectures (82%) and workshops (93%) beneficial to their learning (mean Likert score, 4.18 and 4.64 respectively). Chi-squared analysis showed a significant deviation in student preference from chance for both statements (Lectures: χ 2  = 81.73, df = 4, P  < 0.001; Workshops: χ 2  = 134.01, df = 4, P  < 0.001). The majority of students (76.7%; mean Likert score, 4.15) agreed that “ Attending lectures and workshop classes helped me to understand the course material much better than just reading through or watching the supplied resources ”. Chi-squared analysis showed a significant deviation in student preference from chance for this statement ( χ 2  = 75.30, df = 4, P  < 0.001). The majority of students (85%; mean Likert score, 4.15) agreed that “ The use of in-class interactive tools helped me to understand key course concepts ”. Chi-squared analysis showed a significant deviation in student preference from chance for this statement ( χ 2  = 89.37, df = 4, P  < 0.001). Most students (62%; mean Likert score 2.59) responded negatively to the statement “ Since the lecture and workshop classes were recorded there was no real reason to attend class ”. Chi-squared analysis showed a significant deviation in student preference from chance for this statement ( χ 2  = 48.31, df = 4, P  < 0.001).

To understand how the students felt about the online resources that were provided by the instructor, students were asked to respond to two statements. The majority of students (92.4%; mean Likert score, 4.5), agreed that “ The Instructor provided lecture notes, ebooks, YouTube videos and other resources which helped me to understand key concepts in neurobiology ”. Further, 47.5% of students responded positively to the statement “ I accessed and used the online interactive textbook (Neurobiology: A Case Study Approach) which helped me to understand key concepts in neurobiology ” (mean Likert score, 3.27). Chi-squared analysis showed a significant deviation in student preference from chance for both statements (Resources: χ 2  = 140.86, df = 4, P  < 0.001; Online textbook: χ 2  = 10.14, df = 4, P  < 0.038).

The final statement was designed to assess the student’s overall opinion about face-to-face classes. The majority of students (76%) responded negatively to the statement “ I think face-to-face lectures and workshop classes are an out-dated mode of education in the modern world of information technology, distance learning and self-directed learning” (mean Likert score 1.96). Chi-squared analysis showed a significant deviation in student preference from chance for this statement ( χ 2  = 60.52, df = 4, P  < 0.001).

Factors affecting attendance in lectures and workshops

To determine which factors affected the decision not to attend lectures, students were given a choice of 17 possible options and asked to indicate whether it was “never a reason”, “sometimes a reason” or “definitely a reason” (Table 2 ). In a similar fashion, students were asked about the factors which affected their decision to attend lectures and workshops. For this question they were asked to respond to 14 options with the same three possible responses (Table 3 ).

Various factors influenced student attendance in class. The lecture schedule and the availability of class recordings were reported as the primary reasons for non-attendance. Interestingly, the schedule of the workshop classes was of less concern to students. Of note, work and family commitments were also given as reasons for non-attendance with some students choosing to use the scheduled time for self-directed study instead. Also, of note is that students’ reasons for non-attendance were not related to the standard of teaching in the course, the perceived benefit of attending class, the student’s interest in the content covered in the course, or the availability of online resources. The complete list of options and the distribution of responses can be found in Table 2 .

The perceived benefit gained by attending class, the quality of teaching and the level of interest in the course content played significant roles in determining student attendance. Of note, students who attended class responded positively to the three options related to the active learning activities and participation in class. The complete list of options and the distribution of responses can be found in Table 3 .

Most undergraduate students currently studying at university use technology as an integral part of their daily lives (Seemiller & Grace, 2016 ). These students have a preference for and the ability to use online resources to learn independently and at their own pace (Chicca & Shellenbarger, 2018 ; Seemiller & Grace, 2016 ), are predominantly visual and kinaesthetic learners and tend to embrace gamified, active and interactive learning experiences (Roberts, 2015 ) (Shorey et al., 2021 ). Creating engaging learning experiences is dependent on understanding the needs, interests, and learning preferences of the students we teach.

The global coronavirus pandemic necessitated a rapid pivot to online and blended learning strategies to minimize disruption to student education (Arday, 2022 ). The experience of students during that time is likely to be highly variable and dependent on the individual skill and experience of the instructors in their courses as well as availability of educational technology and virtual learning environments (Koh & Daniel, 2022 ; Sum & Oancea, 2022 ). For some courses and institutions lectures may have been delivered live but online, for others the classes may have been delivered asynchronously with pre-recorded lectures available for students to view in their own time. Thus, student attitudes toward and preferences for online versus face-to-face classes will likely be influenced by this recent experience. However, reflecting on and critically evaluating the factors that motivated students to attend classes before the pandemic can provide valuable insight to inform our decisions as educators whether to continue teaching in the online space or return to the classroom.

Are face-to-face classes an outdated mode of education?

Even before the pandemic, the decline in attendance in face-to-face lectures was well documented with many educators questioning the value of this mode of teaching (Golding, 2011 ; O'Keeffe et al., 2017 ). Many studies attributed the decline in lecture attendance to the increasing availability of digital recordings and other online resources (Edwards & Clinton, 2019 ; Johnston et al., 2013 ). While the provision of these resources offers increased flexibility for students, a common concern has been the potential negative impact this may have on attendance and ultimately student performance (Gosper et al., 2010 ; Kinash et al., 2015 ; Preston et al., 2010 ). A similar trend was observed in the undergraduate neurobiology course analyzed in this study following the university mandated digital recording of lecture and workshop classes from 2013 onwards. However, despite reduced attendance, one of the recurring themes in student feedback was a desire for more discussion and interactivity during class.

With a view to improving the student experience and to encourage students to attend class, an active learning platform was used to augment neurobiology workshop classes to include videos and in-class polling. Overall, student attendance fluctuated during the trimester for an average of 46% which is similar to or greater than other courses in the biosciences (Doggrell, 2019 , 2020a ). While it is not possible to correlate in-class attendance to the use of the active learning platform directly, the survey responses indicated that this mode of teaching was popular among the students. Similar to other studies, attending class was weakly associated with better performance (Doggrell, 2019 ). More importantly, students who chose to attend class did so because of the high standard of teaching, their interest in the course material and thought the classes were beneficial to their learning. There is also a perception among the surveyed students that they will miss important information if they miss class, despite the availability of other resources including class recordings. In contrast, reasons for non-attendance were not related to the quality of teaching, interest in the course content or the perceived benefit of attending class. The main reason for non-attendance were factors outside of the control of the course instructor and included the time of the scheduled lectures (5 – 7 pm on a Monday evening), as well as part-time work and family commitments. An important finding of this study is that the availability of digital recordings and other online resources allowed students with external commitments and time constraints to continue their studies and perform as well as their peers.

Students who did not regularly attend class stated that the availability of digital recordings influenced their decision not to attend. However, a high proportion of students who attended class accessed and used the class recordings, with most indicating that the availability of these resources was not a factor in their decision to attend class. Interestingly, watching the digital recordings was associated with better performance in the course but only if more than 80% of the recording was viewed. Further, students who exclusively used the digital recordings to access the course content had similar academic outcomes to those who came to class. Studies investigating the correlation between lecture attendance and academic performance when lecture capture was available have reported mixed results. A systematic review published in 2020 showed that in the biosciences, 69% of studies show a weak but positive association between lecture attendance and academic performance when lecture capture was available (Doggrell, 2020b ). However, whether students had access to digital recordings was only indicated in 11 of the 29 studies analysed, and no data on how the students used the recordings was presented.

It should be noted that the students in the course take three other courses, some of which have mandatory laboratory classes as well as assessments at varying times during the trimester. However, unlike previous studies, only 9% of students indicated that assessments and demands for other courses was a reason for their non-attendance. In prior studies using a similar survey, 47% of neuroscience students (O'Keeffe et al., 2017 ) and 38% of engineering students (Fitzpatrick et al., 2011 ) indicated that this was a reason for their non-attendance. Further, approximately 30% of students expressed that their decision to attend class was sometimes influenced by assessments in other courses. Throughout the trimester, class attendance fluctuated, and the classes with the lowest student turnout coincided with mid-trimester assessments in other courses.

Since attendance in class was not mandatory and students had access to a variety of digital resources in addition to the class recordings, they had the flexibility to study independently if they chose. However, very few students (~ 11%) stated that the availability of the digital resources was a reason for their non-attendance and only 16% of students stated that they used the time for self-directed study. A notable distinction between students who did not attend class and those that did was their perception of the sufficiency of the digital resources. Only a small percentage of Non-attenders stated that they accessed the YouTube videos (less than 3%) or the interactive textbook (less than 7%) and therefore did not need to attend class. In contrast, approximately 45% of Attenders accessed the YouTube video links, and around 32% of Attenders used the interactive textbook. Interestingly, Attenders viewed these resources as valuable supplements to their learning but still attended class to enhance their overall understanding of the course material.

Do active learning strategies improve the face-to-face learning experience?

One of the strategies that appealed to students the most was the use of an active learning platform during class. The platform was used to facilitate active learning, and to promote student engagement and participation (Shaw et al., 2015 ). Presentation files for each interactive class were uploaded to the platform directly, and multimedia and polling questions were embedded. At appropriate times during the class, the students were polled and given a few minutes to contribute their answers. All answers were anonymous, and students could change their answer if they chose. Multiple choice, short answer and click-on-target style questions were deliberately chosen to clarify key points and to prompt discussion. After a few minutes, the instructor switched to the “live” view of the responses and discussed the correct answers and reasoning with the class. Students could ask questions or seek further clarification and the polling questions commonly prompted detailed discussion of key concepts. While all students were encouraged to log in to the active learning platform during class, it was not compulsory to do so. The classes were automatically recorded and these recording captured the class in its entirety including the in-class polling, answers and resulting discussion. Overall, the interactive workshop classes were very popular with students as the system allowed them to actively participate in class, even though the classes were held in a lecture theatre that was not conducive to active learning (Büchele, 2021 ; Fadelelmoula, 2018 ). Students who attended class indicated that the active learning activities complemented the course resources, helped them to gauge their understanding of key course concepts and factored into their decision to attend class in person. Despite the availability of class recordings, students found greater benefit in attending class than working through the class materials by themselves. Students who did not attend class did not directly experience the benefits of the active learning strategy. Moreover, since their non-attendance was primarily due to external commitments, it is unlikely that the utilization of the platform, or any other teaching strategy, would encourage in-class attendance.

It is interesting to note that the impact of the active learning strategy was not limited to those students who logged in to the platform during class. Students who attended class but did not log in as well as those students who used the class recordings as a substitute for in-person attendance, performed well in the course. One explanation is that the students are learning vicariously by observing their peers’ responses to questions and were thus able to gauge their understanding of key concepts without contributing answers themselves (Mayes, 2015 ; Roberts, 2015 ).

One of the potential limitations of using an active learning platform during class is that encouraging the use of laptops and other devices may be distracting to not just the students using the device but also their peers (Aagaard, 2015 ; Dontre, 2021 ; Fried, 2008 ; Sana et al., 2013 ; Wood et al., 2018 ). A proportion of students (~ 14%) attended class in person and watched more than 80% of each recording. The academic achievement of these students was comparable to those students who either attended class or watched the complete recording. One possible explanation is that despite coming to class in-person, these students were either distracted during class or otherwise disengaged and felt the need to make up the class by watching the recording. However, informal feedback from students suggested that students who came to class used the recordings for review purposes.

In this paper we have examined the impact of a novel teaching strategy designed to improve student engagement in a second-year neurobiology course. This strategy was developed with the preferences of students in mind and included a combination of lectures, technology-enhanced interactive workshops, and online learning resources. Historically, educators have placed emphasis on the value of in-class attendance viewing it as an important indicator for student success (Crede et al., 2010 ; Guleker & Keci, 2014 ). In this study, in line with this belief, students who attended class found the experience to be beneficial to their learning. These students performed well overall, and better than those who did not attend class. However, one of the key outcomes is that students who had to depend on class recordings due to scheduling conflicts or other issues, achieved comparable results to their peers who attended class in person. Consequently, the availability of class recordings and other digital resources enhances flexibility without detrimentally affecting student performance. Students could choose how to access the course content based on their own personal preferences and circumstances and this likely lead to increased engagement and satisfaction with the learning experience. Although the study was limited to a single course, the outcome may be broadly applicable across other disciplines.

Lectures at tertiary institutions are often held in learning spaces that are not conducive to in-class participation (Büchele, 2021 ; Fadelelmoula, 2018 ; O'Keeffe et al., 2017 ). However, leveraging technology to enhance the in-class experience of students has been shown to improve student learning (Wood et al., 2018 ) but the impact is dependent on the students’ engagement with and usage of the specific platform that is implemented (Dunn & Kennedy, 2019 ). In this study, an active learning platform was used to embed in-class polling questions and multimedia at strategic points during workshop classes. The questions and videos were chosen to showcase specific learning outcomes and provide opportunities for students to gauge their understanding of key concepts. Overall, student perception of the interactive workshops was positive, with most students stating that the classes were beneficial to their learning experience. Further, students felt that the in-class experience was enhanced by using the active learning platform and that this mode of teaching helped them to understand and apply the course concepts. The benefits of using this teaching approach is that it can be adapted for use in any discipline that involves both the acquisition and application of knowledge, it is readily scalable to accommodate large classes and can be used for both online and hybrid learning environments. This strategy can also be implemented using various platforms since there are several different in-class polling tools available.

The current generation of students, known for their adaptability to technology and inclination toward independent learning, highly value and often use digital resources (Chicca & Shellenbarger, 2018 ; Seemiller & Grace, 2016 ). Nevertheless, this study reveals that attending face-to-face classes still holds significant value, as students reported greater benefits from in-person interactions compared to relying on independent study. In summary, this research underscores the efficacy of a student-centred teaching approach, leveraging technology and providing flexible access to course materials. By recognizing the evolving preferences and learning styles of students, educators can optimize engagement and learning outcomes in a variety of educational settings.

Availability of data and materials

Ethical clearance for the project was granted by the Griffith University Human Ethics committee and as per human-subject research approval, the student attainment and usage data as well as the raw data from the attitudinal survey is only available upon revision of the protocol.

Abbreviations

Science, technology, engineering, and mathematics

Echo360 Active Learning Platform

Griffith University

IBM SPSS Statistics 28 software Incorporated

Analysis of Variance

Tukey Highest Significant Difference

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Acknowledgements

The author would like to thank Mrs S. Poulsen, Project Leader, Echo360ALP Project, Griffith University Learning Futures for technical assistance, execution, and feedback on the integration of the active learning platform in the classroom as well as the students in 2020MSC Neurobiology for their participation, patience, and feedback.

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Lewohl, J.M. Exploring student perceptions and use of face-to-face classes, technology-enhanced active learning, and online resources. Int J Educ Technol High Educ 20 , 48 (2023). https://doi.org/10.1186/s41239-023-00416-3

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• Asynchronous learning
• Synchronous learning
• Attitudinal survey
• Lecture capture

Online university education is the new normal: but is face-to-face better?

Interactive Technology and Smart Education

ISSN : 1741-5659

Article publication date: 2 August 2021

Issue publication date: 4 October 2021

Following the rapid shift to online learning due to COVID-19, this paper aims to compare the relative efficacy of face-to-face and online university teaching methods.

Design/methodology/approach

A scoping review was conducted to examine the learning outcomes within and between online and face-to-face (F2F) university teaching programmes.

Although previous research has supported a “no significant difference” position, the review of 91 comparative studies during 2000–2020 identified 37 (41%) which found online teaching was associated with better learning outcomes, 17 (18%) which favoured F2F and 37 (41%) reporting no significant difference. Purpose-developed online content which supports “student-led” enquiry and cognitive challenge were cited as factors supporting better learning outcomes.

Research limitations/implications

This study adopts a pre-defined methodology in reviewing literature which ensures rigour in identifying relevant studies. The large sample of studies ( n = 91) supported the comparison of discrete learning modes although high variability in key concepts and outcome variables made it difficult to directly compare some studies. A lack of methodological rigour was observed in some studies.

Originality/value

As a result of COVID-19, online university teaching has become the “new normal” but also re-focussed questions regarding its efficacy. The weight of evidence from this review is that online learning is at least as effective and often better than, F2F modalities in supporting learning outcomes, albeit these differences are often modest. The findings raise questions about the presumed benefits of F2F learning and complicate the case for a return to physical classrooms during the pandemic and beyond.

Digital learning

• Teaching methods
• Universities
• Higher education

Stevens, G.J. , Bienz, T. , Wali, N. , Condie, J. and Schismenos, S. (2021), "Online university education is the new normal: but is face-to-face better?", Interactive Technology and Smart Education , Vol. 18 No. 3, pp. 278-297. https://doi.org/10.1108/ITSE-08-2020-0181

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Rapid technological developments in digital education have seen wide-spread adoption of blended and fully online content across a range of educational institutions, including universities. Advocates of online learning (OL) cite a range of key advantages including greater access, cost-effectiveness and the creation of a democratised “community of learners” able to operate in real-time and asynchronous modes ( Beishuizen, 2008 ; Hass and Joseph, 2018 ). In 2020, the inherent agility of OL came into sharp focus with the international impacts of Covid-19. As a result of the pandemic and almost overnight, online university teaching has become the “new normal”. This rapid shift supported critical business-continuity in the sector, but some argued that it largely completed and was enabled by, the full structural integration of digital education that had been proceeding for decades ( Brown and Duguid, 1996 ; Hiltz and Turoff, 2005 ; Kaplan and Haenlein, 2016 ). While blended modes are quite commonplace in countries such as Australia ( Crawford and Jenkins, 2017 ), the UK ( Adekola et al. , 2017 ), Italy ( Previtali and Scarozza, 2019 ) and Singapore ( Jones and Sharma, 2019 ), many educators and higher education institutions faced both full online delivery, as well as the pedagogical implications of teaching digitally for the first time ( Dhawan, 2020 ). A raft of questions comes to the fore. Are some educators still “resistant” ( Blin and Munro, 2008 ) to digital learning in the contexts of a pandemic? When Covid-19 is still in circulation, why are there calls and initiatives to get students and staff back to campus and physical classrooms? What is the current evidence-base in support of OL? Are its learning outcomes broadly equivalent to face-to-face (F2F) modes and, if so, what is the case for a post-Covid return to F2F teaching?

A long-standing criticism of OL is that it cannot replace F2F modes because it lacks the capacity for the communicative processes that occur with physical presence; processes through which the cognitive, meta-cognitive and social/interactive aspects of learning optimally occur ( Francescato et al. , 2006 ). Learning is situational and materially embedded in the context of the classroom and being physically co-present to learn with fellow students and teaching staff ( Taylor, 2013 ). This is not to say that OL is not situated; it also takes place somewhere and has a physicality and materiality to it. While such presence appears important, it has also been argued that F2F teaching frequently defaults to a “teacher-centred” approach that promotes a passive, disengaged relationship between students and educational content; a factor contributing to poor comprehension and information retention ( Garrison and Cleveland-Innes, 2005 ). There is growing evidence that well-structured online courses which promote “active learning” (characterised by group problem-solving requiring higher-order thinking, task completion and reflection) and have high perceived levels of tutor leadership (or “presence”), achieve learning outcomes that are equivalent to or better than, those achieved via F2F teaching ( Cleveland-Innes and Emes, 2005 ; Garrison and Cleveland-Innes, 2005 ; McLaughlin et al. , 2013 ; Thomas and Thorpe, 2019 ). A comprehensive review of the history of online teaching and learning is beyond the scope of this paper, however, we highlight key aspects of the debate and evidence regarding the relative efficacy of F2F and online modalities, as well as “blended” approaches and their potential to optimise learning outcomes.

The great debate – does mode matter?

Since the early 1990s, much of the consideration regarding the comparative efficacy of digital education has been framed around a wider controversy often referred to as the “Clark/Kozma debate”. Clark’s original meta-analysis on the influence of media on learning ( Clark, 1983 ) led him to conclude that media do not influence learning outcomes under any circumstances. In what came to be known as the “no significant difference” position, Clark proposed that researchers should cease exploring the relationship between media and learning, unless they could also provide substantive theory as to why media-specific differences exist ( Clark , 1983, 1994 ). The debate “proper” commenced in 1991 when Kozma outlined a learner “interaction” theory which proposed a synergistic relationship between media, content and the learner ( Kozma , 1991, 1994 ). He argued that different media have distinct symbolic/relational systems and processing, that may both compliment an individual learner and provide effective learning experiences.

Kozma’s theory has been highly influential in framing the social/interactive theory behind blended learning and active learning approaches, including recent initiatives regarding online “community of inquiry” teacher/student collaborations to achieve educational outcomes ( Rubin et al. , 2013 ). Despite such developments, the evidence has remained largely inconclusive regarding any single media (i.e. primary F2F and OL formats) being capable of producing significantly better learning outcomes. While some studies have reported significantly poorer learning outcomes for online university courses ( Brown and Liedholm, 2002 ), such findings have been in the minority. Meta-analyses after 2000 examining learning outcomes ( Bernard et al. , 2004 ; Shachar and Neumann, 2003 ) typically concluded that modality, per se , was not a significant factor in learning outcomes; findings Clark drew upon to re-iterate his original claims ( Clark and Feldon, 2005 ; Clark, 2007 ). This position statement remains essentially unchanged ( Becker, 2010 ; Clark, 2014 ), despite some recent meta-analyses showing that university learning outcomes are generally better with OL modes ( Jayakumar et al. , 2015 ; Jurewitsch, 2012 ; Nguyen, 2015 ). Critics of this status-quo argued that Clark’s commentary reflected a lack of understanding regarding educational applications of “new media” and their educational applications (e.g. gaming platforms and social media) which may be found to provide qualitatively distinct outcomes ( Becker, 2010 ; Rideout et al. , 2010 ) and, many of the claimed “no difference” findings were drawn from studies with poor methodology (e.g. non-random selection, poor control of teacher/student variables and matching of content and contact hours) or which focus on aggregate-level outcomes (e.g. student course grades, tutor/student satisfaction) which may not identify process elements of specific media that are uniquely beneficial ( Francescato et al. , 2006 ; Garrison and Cleveland-Innes, 2005 ; Mullen, 2019 ).

More recent studies have tended to explore the factors associated with developing and delivering a student-centred curriculum that may be associated with optimal learning outcomes, irrespective of the primary teaching mode used. Educators are using digital technologies and new social media platforms to reconceptualise and reconstitute teacher-student relationships and extend learning conversations beyond the traditional classroom ( Condie et al. , 2018 ; Graham, 2014 ). Positioning higher education students as “colleagues in training” ( Condie et al. , 2018 , p. 14) and “students as producers” ( Hynes, 2018 ) is more possible with the affordances of the “participatory web” ( Costa, 2014 ) and within open digital educational practices ( Cronin, 2017 ). As such, earlier constructivist models of individual computer-assisted learning ( Crook, 1998 ) have been relegated in favour of cooperative learning based on social learning theory. These models posit that highly effective learning occurs through interactive work with others and shared task completion and reflection.

Cleveland-Innes and Emes (2005) found that social and academic interaction were critical factors in achieving quality educational outcomes, irrespective of whether the learning environments were F2F or online. However, related research ( Garrison and Cleveland-Innes, 2005 ) has shown interaction, per se , is not sufficient to achieve the kind of critical discourse (and related critical thinking) needed to achieve “deep learning” ( Biggs, 1998 ). Both student-student and tutor-student interactions are important in the creation of critical discourse. However, research indicates that the perceived structure and cohesion associated with the tutor role (often defined as teacher leadership or “presence”) is a stronger predictor of critical discourse and overall effectiveness of both OL and F2F teaching ( Hay et al. , 2004 ), but possibly a greater predictor in online environments ( Thomas and Thorpe, 2019 ; Wu and Hiltz, 2004 ). Educator presence, rapport and a sense of community and trust amongst learners are essential for effective digital learning experiences ( Lambrinidis, 2014 ; Ragusa and Crampton, 2018 ; Stone, 2017 ).

The aim of this review is to compare university learning modes, such that the substantive comparison is between fully online and F2F delivery. As such, our search is limited to studies involving a reasonably rigorous approach to comparing these modalities, that is, using an experimental or quasi-experimental design. Our research question is whether, based upon aggregate findings during the period 2000-2020, fully online or F2F learning modes are more effective in achieving commonly recognised learning outcomes such as test grades and course marks. In posing this comparison, we are mindful that digitised learning commonly blends these approaches, but our primary question goes to the matter of a possibly unique contribution of F2F modes and how this may inform future use of this format, particularly in a post-Covid environment.

This review adopts a scoping review methodology as outlined by Arksey and O’Malley (2005) . The scoping method follows a structured approach to map and presents a descriptive or summative overview of the literature on a topic. This method has been adopted across disciplines and is being increasingly recognised as an effective method when compared to a literature review ( Pham et al. , 2014 ). The focus of this review was to use existing data sources to address our research question, i.e. secondary data sources and did not include any primary data. The scoping process has four stages: identifying the research question; identifying relevant studies; study selection and charting the data and collating, summarizing and reporting the results. This process allows transparency and clarity of data collection, study selection and the collating of results.

Terminology

There are a plethora of terms used to refer to these respective teaching modalities, as well as those which combine their use. Throughout this paper, we use the following standard terms to delineate teaching modes and roles; traditional, F2F and OL; combined F2F/OL (“blended” but also “flipped” when indicated); and teaching practitioners (“tutors” or “instructors”). We use the term OL in preference to e-learning and digitised learning as the latter is less specific to mode and often applied to blended formats. The terms “learning” and “teaching” are typically used as presented by the authors. Unless otherwise specified, “course” refers to a single unit of study (e.g. one-semester Introduction to Sociology unit).

Identifying the research question

The review examined the outcomes and relative efficacy of online (web-based) and F2F university teaching.

Identifying relevant studies

Google Scholar was used as the primary search engine with advanced search options. Google Scholar is multidisciplinary and has broad coverage across health, social science and education. The education-specific database of the Education Resources Information Centre (ERIC) was then used to identify any education-related studies that could have been missed in the primary search via Google Scholar.

Inclusion and exclusion criteria

Search items were restricted to articles that were: journal articles, published research dissertations and reports; published in the English language; published between 2000 and 2020; and based on key terms. The stated inclusion criteria allowed us to limit the search output to a manageable number of relevant items. This was particularly important regarding the restriction to “title search” only, as earlier searches of full-text articles produced a large number of articles (in the thousands).

Search terms

Primary search involved keywords combination: “online” x “face to face” x “learning” x “comparison”. Key synonyms were also used in a number of other combinations, notably: “eLearning”, “internet”, “web-based”, “teaching”, “traditional” (i.e. teaching), “experiment”, “outcomes”, “review” (to identify relevant descriptive/systematic reviews and meta-analyses). The selected search terms also reflect the review requirement which involves an experimental design or other direct comparisons of the relative learning outcomes of OL and F2F teaching.

Study selection and charting the data

The scoping review included studies that were within the parameters of our enquiry, with all other articles being excluded. These could be studies that did not directly compare teaching modalities; where the teaching modalities were unclear or did not provide a substantive comparison of the two modalities; or where the study predominantly involved non-tertiary students (e.g. high school students). Only journal articles, published research dissertations and reports were included. All other document types including books, non-empirical book chapters, articles that could not be found/opened and studies in a language other than English were excluded.

Our review was conducted in two stages; a preliminary search followed by a combined analysis. The preliminary search yielded 76 studies which, after review, were reduced to 28 relevant items ( Table 1 ). These 28 studies included 1 scoping review and 6 meta-analyses, which constituted a substantial number of individual studies that warranted more detailed review and the inclusion of those that met our study criteria. This combined analysis found a total of 131 studies for review. A number of studies were then excluded for these reasons; published before the year 2000 (4 studies), insufficient information in the meta-analysis to draw clear determinations or the individual paper was not accessible (10 studies), duplicates (6 studies) and outside the scope of this study such as non-university population (1 study) or not directly comparing primary OL and F2F conditions (e.g. using blended learning as a primary condition (19 studies). For the purposes of this analysis, a meta-analysis or scoping review was counted as a separate study/finding (i.e. 7 studies) in addition to the studies it contained and which met out criteria. The combined analysis yielded a final sample of 91 individual studies for review ( Table 2 ).

Collating, summarising and reporting the results

This review narrates the findings from the included studies using an approach consistent with Snilstveit et al. (2012) . The following method was used for collating and reporting the results. For individual studies, the results of statistical analyses and/or author direct statements were used to determine whether learning outcomes favoured OL or F2F or no significant difference was observed. To determine if an individual study within a meta-analysis favoured one mode, the effect sizes (g) were used (i.e. positive effect sizes were counted in favour of OL and negative in favour of F2F). Direct statements by the meta-analysis authors were also used. With regard to the outcome measures used to assess university student learning, the most recognised performance metrics used in meta-analyses are the scores of standardised tests, grade point average (GPA) or overall course grade ( Jayakumar et al. , 2015 ; Jurewitsch, 2012 ). Other measures assess specific learning processes such as cognitive/metacognitive processes ( Kurt and Gürcan, 2010 ) and stress adaptation ( San Jose and Kelleher, 2009 ). A range of other student self-report measures are also used such as satisfaction, confidence, knowledge and performance. Some authors of meta-analyses state that finding and qualifying performance measures between studies can be an issue ( Jayakumar et al. , 2015 ; Jurewitsch, 2012 ; Nguyen, 2015 ). Table 2 details the range of learning outcomes identified in these studies. While the meta-analyses include studies published in 2000–2015 there are several individual studies published between 2015 and 2020 that are included in this study, therefore expanding the time-horizon of previous research.

The combined analysis reviewed individual study reports, as well as the constituent individual studies within relevant meta-analyses and the scoping review. This identified an initial pool of 131 studies. This was reduced to a final sample of 91 studies, which met our inclusion criteria. From these, a total of 37 studies (41%) found online teaching was associated with better student learning outcomes, 17 studies (18%) reported better outcomes with F2F and 37studies (41%) found no significant differences ( Table 2 ). Summary findings from these respective categories are detailed below, including the breakdown of results within the composite studies.

Face-to-face

Addis (2009) and Callister and Love (2016) found that F2F Elementary Education students performed better. The Addis (2009) study found gains in student learning were pronounced in each condition, but the F2F group significantly outperformed the online group on post-test scores due to easier collaboration in the F2F setting and OL students taking longer to get accustomed to the new mode. Callister and Love (2016) compared four master’s level Negotiation classes at two universities but taught by the same professor. Test scores indicated that F2F learners achieved higher negotiation outcomes than online learners. The researchers attributed this to increased instructor interaction and reduced hostility in the F2F settings, even when using the same technology (Google Chat).

Bond and Peterson (2004) found that F2F learners displayed better mastery of subject matter. The study assessed the quality of the problem-based learning (PBL) unit (for teaching delivery) based on several indices. The subjects of the study were university students in an Instructional Planning class. The study authors concluded that the on-campus group selected a wider variety of instructional materials, planned more detailed instruction, used more pedagogical terminology and placed a higher value on planning. They argued that observing the teacher and emulating the teacher’s preparation methods led to these differences in performance. Both groups were similar in problem selection, length of unit, number of materials, organisation of student groups and integration of technology. McKenzie (2013) found that medical students in F2F classes gave significantly higher ratings to teaching staff and reported greater knowledge attainment, which was supported by higher test scores. The researchers suggested these differences may have been due to both technical limitations with the online version of the course (which constrained the complexity of online activity) and the greater complexity and feedback opportunities that were permitted within the F2F course. Despite this both groups reported similar levels of confidence.

San Jose and Kelleher (2009) set up an experimental comparison based on the ecoshock index, a 12-item measure of stress adaptation to new learning environments, developed and tested to measure differences in university students’ responses toF2F and OL learning ecologies. They found that online students reported greater adaptive stress (ecoshock). The index yielded promising internal reliability scores in pilot testing and experimental conditions. Construct validity was supported with evidence from within-subjects experimental comparisons (N = 49) showing that ecoshock was significantly higher online compared to F2F conditions, as the authors had predicted. Also as predicted, ecoshock correlated negatively with an 8-item index of affective learning, which was found to be greater in F2F conditions than OL conditions. While such factors could potentially undermine learning outcomes(the authors, citing Fontaine, 2000 ), this report does not provide information as to whether higher reported adaptive stressor lower affective learning was associated with poorer performance.

In addition to these individual studies, the dis-aggregation of the meta-analyses produced 12 additional findings that met our inclusion criteria and reported results favouring F2F delivery; Lack (2013 : 1 study), Means et al. (2009 : 7 studies), Nguyen (2015 : 2 studies) and Voutilainen et al. (2017 : 2 studies).In total, 17/91 studies (18%)found that OL was the more effective delivery mode.

Heckman and Annabi (2005) found that asynchronous learning networks (ALN) executed through a web-based application generate high levels of cognitive activity equal to and in some cases superior to, the cognitive processes in the F2F classroom. The study also found that student-to-student interactions contain a greater proportion of high-level cognitive indicators than do student-to-teacher interactions. These cognitive indicators are grouped on different hierarchical levels corresponding to the respective level of cognitive activity. These commence with exploration and analysis and culminate with integration as the highest level of activity. Similarly, Williams and Castro (2010) investigated teams of organisational behaviour students about their perceived team performance and concluded that relationships in online teams were better. The authors found that “the flexibility provided by the online environment might allow for more ongoing learning and more frequent exchanges” (p. 141) than in F2F contexts. Team setting moderated the relationship that member teamwork orientation and member social interaction had on individual team-source learning; the relationships were stronger in online teams.

Raynauld (2006) investigated an Economic Policy and a Finance course where the author found that online students perform better (in terms of final grade) in Economic Policy, but there are no significant differences in Finance. While the online version of the Economic Policy course was well-tailored towards the needs of online students, it was the first time that the Finance course was conducted through an online format. This led the authors to propose that the type of course, the setup of the learning environment and the assessment decisions influence the success of online courses.

The scoping review of Nguyen (2015) found online delivery to be at least as effective as F2F delivery. From the 22 constituent studies, 5 met the inclusion criteria for this scoping review, of which 2 favoured OL, 2 favoured F2F and 1 found no significant differences. As the overall finding of the meta-analysis was also in favour of OL, it is categorised as an OL finding within our combined analysis. The report author offered this assessment, “Taken as a whole, there is robust evidence to suggest online learning is generally at least as effective as the traditional format” (p. 309).

The meta-analysis of Jayakumar et al. (2015) found that online students performed better. From the 38 constituent studies, only 3 met the inclusion criteria for this scoping review and all were in favour of OL. Jurewitsch (2012) drew a similar conclusion in his meta-analysis and found that online students performed better in problem-based learning. From the 5 constituent studies, only 3 could be located and accessed and all of them favoured OL. Overall effect size was found to be slightly in favour of online problem-based learning in terms of student performance outcomes (test scores).

The dis-aggregation of the other meta-analyses produced 24 findings that met inclusion criteria and supported OL delivery; Lack (2013 : 3 studies), Means et al. (2009 : 16 studies), McCutcheon et al. (2014 : 2 studies) and Voutilainen et al. (2017 : 2 studies).In total, 37/91 studies (41%)found that OL was more effective than F2F delivery.

No difference

In total, 11 studies, which used standard student performance metrics(e.g. test mark, final grade) found that there were no significant differences between F2F and OL modes ( Cavanaugh and Jacquemin, 2015 ; Driscoll et al. , 2012 ; Ghonsooly and Seyyedrezaie, 2014 ; Herman and Banister, 2007 ; Horspool and Yang, 2010 ; Johnson et al. ,2000 ; Pilbeam and Barrus, 2010 ; Rosell-Aguilar, 2006 ; Sussman and Dutter, 2010 ; Woolsey, 2013 ; Yen et al. , 2018 ).

Cavanaugh and Jacquemin (2015) examined a teaching database with information from 140,444 students enrolled across 6,012 university courses and taught by over 100 faculty members between 2010 and 2013. Notable findings were that students with higher GPAs perform even better in online courses or alternatively, struggling students perform worse when taking courses in an online format compared to a F2F format. Driscoll et al. (2012) conducted a quasi-experimental study of introductory Sociology students and found that differences in student performance between the two modes may be due to a selection effect. Herman and Banister (2007) found no difference in learning outcomes, post-graduate Education students. Johnson et al. (2000) found a similar result for students in a Human Resources course and noted that while those in F2F courses held slightly more positive perceptions about their tutors this did not affect course grades. Pilbeam and Barrus (2010) found that while grades in Computer Literacy courses varied little between modes the percentage of “A” grades were higher in F2F.

Nine studies used outcome measures, which assessed learning processes, mode-related adaptation,and a range of subjective appraisals of performance or engagement ( Driscoll et al. , 2012 ; Ghonsooly and Seyyedrezaie, 2014 ; Groves et al. , 2014 ; Horspool et al. , 2010; Johnson et al. , 2000 ; Kurt and Gürcan, 2010 ; Rosell-Aguilar, 2006 ; Woolsey, 2013 ; Yen et al. , 2018 ).

Kurt and Gürcan (2010) and Ghonsooly and Seyyedrezaie (2014) found that there were no significant differences in student learning strategies between F2F and OL. Kurt and Gürcan (2010) investigated the relationship of undergraduate students’ success with learning strategies and computer anxiety. No significant difference regarding cognitive and metacognitive learning strategies (assessed with separate scales developed by Namlu (2005) ) was found. However, the authors did find average scores for learning anxiety were significantly lower in the F2F instruction group. Ghonsooly and Seyyedrezaie (2014) found that there were no significant differences between the two groups of learners regarding preferences for language learning strategies and reading comprehension. The study measured the outcome of 200 language students with a 50-item translated version of the Strategy Inventory for Language Learning and a test of reading comprehension.

Rosell-Aguilar (2006) found that there were few differences between F2F and OL. The subjects were undergraduate students in a language course. The study found that there were not many differences between online andF2F learners but there are differences in course marks; 10% moreF2F learners achieved a distinction than online learners. Online learners expressed less intention to miss tutorials than theirF2F counterparts. However, more online learners never attended at all. There was a higher number of students who wished to switch from online to F2F rather than vice-versa suggesting a quarter of the online students did not have a good enough experience with the online tuition to wish to continue using the medium. In this and several other studies, it was noted that the students voiced a preference for F2F tuition but this statement was often qualified by stating that the quality of the online course was lacking (often because it was the first time the course was offered via an OL format). Groves et al. (2014) found that there were no significant differences in spiritual awareness between F2F and OL. The primary sample was health-care students and the study concluded that such awareness was achieved independently of the mode of course delivery.

The meta-analysis of McCutcheon et al. (2014) found no significant difference in nursing student performance measures examined across 19studies. Five of these met our inclusion criteria, of which 3 found no significant differences and 2 supported OL. The authors mention that the variation of the intervention made comparison difficult and that there is a clear need for well-structured and controlled research. Notably, the combined evidence suggests that online learning for teaching clinical skills is no less effective than traditional means. At the same time, this review highlights a broader lack of available evidence on the implementation of OL to teaching clinical skills in undergraduate nurse education and the need for further research in this area.

Means et al. (2009) is the largest available meta-analyses and determined that there were no significant differences between teaching modes. It includes 27 studies across a range of courses, which directly compared F2F vs OL conditions and found a small but non-significant effect in favour of OL modes (a mean effect of +0.05 and p = 0.46) This led the authors to conclude that “instruction conducted entirely online is as effective as classroom instruction but no better” (p. 18) Amongst these 27 studies 23 met our inclusion criteria of which 16 favoured OL and 7 supported F2F.

The two remaining meta-analyses were unable to draw a clear conclusion from the available data. Lack (2013) concluded that due to the difficulty of drawing on comparable results between the studies, the overall result of the study was unclear. However, of the 30 included studies, 20 met the inclusion criteria of the current review and did draw a clear finding. Amongst this group 16 found no significant differences, 3 favoured OL and 1 supported F2F. Voutilainen (2017) investigated 9 health-care studies of which 4 met the inclusion criteria of this scoping review. The review indicated that 2 studies supported OL and 2 studies supported F2F. The meta-analysis resulted in the weighted mean of 5.24 (0.13-10.3, CI) on a 0–100 scale, indicating that e-learning improved the knowledge/skill scores 5.24 points more than conventional learning, on average. However, as the range of the weighted mean was wide (−11.2 to 21.7), the authors concluded that generalisations could not be drawn. The authors of both meta-analyses indicated that the various studies differed substantially and that the results were too situational to make claims as to generalisability.

The dis-aggregation of the one remaining met-analysis in this category found a single study, which observed no significant difference ( Nguyen, 2015 ; 1 study). The combined analysis found 37/91 studies (41%) in which no significant differences were observed between the two teaching modes.

The primary focus of our review was to determine, based on the weight of evidence over the past two decades, whether F2F or online teaching modalities provide greater efficacy regarding university learning outcomes or whether the “no significant difference” position ( Clark , 2007, 2014 ) continues to reflect their relative status. Our combined analysis provided a clear answer to our research question. From the 91 identified studies which directly compared these two teaching modes, 37 (41%) found online teaching was associated with better learning outcomes, 17 (18%) favoured F2F teaching and 37 studies (41%) found no significant differences. Following the early “debate” of Clark (1983) and Kozma (1991) and more recent findings that modality, per se , is not a significant factor in learning outcomes ( Bernard et al. , 2004 ; Clark, 2007 ; Shachar and Neumann, 2003 ) the current data indicate that, in aggregate terms, online modalities are producing better learning outcomes for university students. Also consistent with the original thesis of Kozma (1991) are recent findings that the better outcomes associated with online learning are possibly due to the qualitatively different relationship that appears to develop between this media, the learning content and the learner ( Cavanaugh and Jacquemin, 2015 ; Heckman and Annabi, 2005 ; Jurewitsch, 2012 ; Nguyen, 2015 ; Williams and Castro, 2010 ).

If online university education appears to provide consistently better learning outcomes than F2F approaches, what factors may contribute to this difference? There is some evidence that OL and the working environments that it creates enable higher levels of cognitive activity which can lead to better performance. Heckman and Annabi (2005) found that student-to-student interactions foster higher cognitive activity and that students assume some parts of the teacher role in OL modes. Small group learning (e.g. “break-out” groups, team tasks) may afford a greater comfort in learning from peers, particularly as they are often distinctly “separate” spaces in the OL environment. This may allow higher functioning students to provide greater input and direction to the process, reinforcing their own learning and content-related leadership, while other students may feel more confident to address questions and uncertainties directly to fellow students ( Jurewitsch, 2012 ; Nguyen, 2015 ). This appears consistent with findings by Cavanaugh and Jacquemin (2015), who observed that well-performing students perform better when taking the online version of a class, although one small study (Kurt et al. , 2010) reported lower aggregate levels of learning anxiety in a F2F course. Another argument as to why OL results in better student performance is that geographical distance does not play a role in student enrolment, and therefore the range of potential students is increased ( Jurewitsch, 2012 ). Similarly, the flexibility of synchronous and asynchronous OL has been found to be associated with increased student performance ( Nguyen, 2015 ). This is consistent with the finding from Williams and Castro (2010) that OL allows “for more ongoing learning and more frequent exchanges” (p. 141).

Nguyen (2015) and Jurewitsch (2012) state that OL plays a specific role in enabling better performance by providing an accessible and safe learning environment. Both studies found OL can cater to more learning styles, enables learning through a variety of formats and materials and can more readily cater to individual learning needs, all of which contribute to increased performance. As such, it is critical that the course setup is well-tailored to the specific needs and strengths of OL environments, rather than simply transferring F2F-developed content to online platforms. Several of the included studies reported good and poor translations of such content to online platforms and their effects on student performance ( Jayakumar et al. , 2015 ; Jurewitsch, 2012 ; Raynauld, 2006 ). Raynauld (2006) concluded that the setup of the learning environment is a key determinant for student performance in online courses, as measured by final grade. He found that well-established F2F and OL Economics courses favoured OL, but an established F2F Finance course compared with an OL version delivered for the first time showed no difference, possibly indicating the advantage of online consolidation was lost(albeit the cross-compared courses were different). Jayakumar et al. (2015) found that the web offers significantly more tools for teaching and learning and that the combination of teaching methods is what creates superior performance outcomes for OL courses. Jurewitsch (2012) reported that optimal group size to support problem-based learning and the right mix of synchronous/asynchronous interactions with tutors are amongst key factors which result in superior performance with OL. Moreover, while digital technologies have developed rapidly, student cohorts are increasingly highly adapted to them and able to draw out the best of what these evolving platforms have to offer ( Jurewitsch, 2012 ; Yen et al. , 2018 ).

Challenges and opportunities with Covid-19

In a review of the pedagogical responses of 20 countries within the “intra-period Covid-19 response”, Crawford et al. (2020) found a range of approaches have been taken by universities, which are highly dependent upon their respective country’s political decisions on Higher Education policy, infection rates and pandemic control measures. In countries such as Jordan, the pandemic is enabling an arguably overdue digitalisation of Higher Education. In places such as Australia, there is a desire to keep campuses open and get back to F2F teaching with physical distancing protocols as soon as possible. The reasons for returning to or maintaining F2F teaching are complex. These range from (often unfounded) presumptions of teaching quality and student preference, to concerns about wider social and economic implications of moving away from physical campuses. This disruption is mirrored in the corporate sector where some companies, based on their pandemic experiences, now see a future with “much less real estate” ( Schatzker, 2020 ).

It is notable that while several studies in our review found the physical presence of a tutor conferred distinct learning advantages ( Addis, 2009 ; Bond and Peterson, 2004 ; Callister and Love, 2016 ) this was only observed in a small proportion of the total sample. More direct time with instructors and the ability to observe and emulate their practice (e.g. elementary school educators) were cited advantages, although some of these same studies noted adjustments to new online formats and technical constraints may have affected their relative outcomes. While newer digital platforms have likely reduced such gaps( Williamson, 2019 ), online delivery has the potential to compromise academic quality and curriculum standards, particularly where academics are overworked, inexperienced and/or unsupported by their institutions to make the digital transition. The rapid move of some teaching to fully online formats during the pandemic presents substantial risks in this regard and the heightened need for sharing good practice. Crawford et al. (2020) advise that universities “need to be conscious of their ability to continuously monitor the quality of the learning design” (p. 20) in such times of rapid change and uncertainty.

By grounding our review in the empirical comparisons between online and F2F delivery, we travel some way beyond Burns (2020) critique of the utopian discourse that digital technologies can and will save us and can and have saved higher education “the first time from austerity funding models, the second time from a pandemic” (p. 247) to find that many aspects of digital education are beneficial for student learning. However, there is also a warning here; that when digital technologies meet neoliberal policy reforms, academia and academics may not be able to respond to the kind of political-economic restructuring that follows ( Kornbluh, 2020 ). Beyond the surface issue of learning modalities, there is much at stake for higher education when it is reworked by the application of digital technologies for neoliberal purposes in a pandemic.

Limitations and future research

A broad limitation of the review findings relates to the substantial variability that exists regarding research-related terminology, examined learning processes and outcomes and the associated measures used to determine learning efficacy. This high variability made it difficult, within the scope of the current analysis, to be able to meaningfully compare outcomes between some studies. This issue has been highlighted by several researchers examining this topic ( Jayakumar et al. , 2015 ; Jurewitsch, 2012 ; Nguyen, 2015 ). The comparability limitations risk a lack of “critical mass” regarding well-aligned and controlled studies and an ability to address key issues at a level of detail. Many of, which did not randomly allocate subjects or otherwise control for variables, which could potentially confound outcomes (e.g. mode self-selection). For example, there is evidence of student preferences towards online courses, due to their convenience and other factors ( Jurewitsch, 2012 ) and that better students may adjust more readily and perform better in digital education ( Cavanaugh and Jacquemin, 2015 ). Similarly, better educators may adapt more readily to the online environment and are better capitalise on the learning advantages it offers. Such factors indicate that wider determinations regarding relative efficacy must be drawn with caution and with recognition of these existing limitations with study methods. As noted, future research could resolve some of this uncertainty using well-controlled studies. Such research could also move beyond the coarser indicators of learning effectiveness (e.g. final grade) to examine process elements associated with optimal learning such as group interactive processes ( Shea and Bidjerano, 2012 ) and cognitive analysis and integration in these contexts ( Heckman and Annabi, 2005 ; Kurt and Gürcan, 2010 ). Research is also needed to determine optimal mode combinations within synchronous hybrid OL/F2Fmethods such as Hyflex ( Beatty, 2014 ), including clarification of the “best use” of F2F delivery, particularly as classroom delivery will increasingly be embedded within such formats ( Brown et al. , 2020 ). Another limitation is that our analysis rests on a binary distinction between F2F and online teaching modes. While reflecting on a current reality for some educators, it is an increasingly difficult boundary to maintain given how integrated and enmeshed we are in digital infrastructures, systems and devices within our everyday lives. Costa et al. (2019) call for more advanced theoretical work around technology in education that goes beyond modes, individual tools and binary distinctions and conceptualise technology and its pedagogical impacts “in more nuanced and critical ways” (p. 396) to make new possibilities for higher education in the present and foreseeable future.

Conclusions

Our findings indicate that there is little consistent evidence after the year 2000 that F2F university teaching supports better student learning outcomes. Conversely, there is evidence at an aggregate level that OL is at least as effective and often confers a modest advantage compared with F2F modalities across a range of study disciplines. These results can inform university educators and administrators as to the broad-based efficacy of this teaching mode, particularly as the pandemic has brought its use and value into sharp focus. While it is possible that the current findings reflect forms of systematic bias, mitigating against this conclusion is the aggregate nature of these results; mode-specific findings favouring OL outcomes at a 2:1 ratio when compared to F2F delivery. Key factors within this appear to be the role and “presence” of online tutors and their capacity to create “well-scaffolded”, engaging learning activities, particularly those conducted through small-group interactive tasks which develop independent learning skills. Peer facilitation developed this way may be one of the best strategies to encourage participation, while also freeing the teacher’s role to focus on developing consensus or specific learning outcomes. Importantly, such student-to-student interactions appear to generate higher levels of cognitive challenge and activity, with the reviewed evidence indicating these specific relationships were often stronger in online team environments.

While the current findings highlight the mounting evidence-based regarding online learning, further research is needed to support its conclusions but also to better understanding the constituent elements contributing to effective learning outcomes across modalities and within hybrid approaches. This requires a greater body of well-designed studies with large, cross-institutional samples that can support statistically significant findings. These should also provide a detailed examination of interactive-process elements, including peer facilitation and teacher leadership/presence, their relationship with learning outcomes and whether mode-specific factors enable such processes. A final question of interest to our research group is whether the learning of instrumental (“hands-on”) skills is better achieved through F2F modes. The current review included studies, which found skills-based learning outcomes (e.g. musical performance and medical procedures) were similar in online and F2F modes, but the evidence-base remains limited. As university learning increasingly shifts to digitised formats, this is a key issue affecting higher education and industry sectors alike and is the focus of further research within our team.

Primary search combinations and relevant studies identified

Summary of primary university learning outcomes by teaching mode

In composite studies/meta-analyses – indicated outcome variable is that most used

Composite/meta-analyses report overall efficacy finding; results of included individual studies were:

Means et al. (2009) F2F(7); OL(16); ND(0)

Jurewitsch (2012) F2F(0); OL(3); ND(0)

Lack (2013) F2F(1); OL(3); ND(16) [no clear determination from assessed data]

McCutcheon et al. (2014) F2F(0); OL(2); ND(3)

Jayakumar et al. (2015) F2F(0); OL(3); ND(0)

Nguyen (2015) F2F(2); OL(2); ND(1) [categorised OL based on main study finding]

Voutilainen et al. (2017) F2F(2); OL(2); ND(0) [no clear determination from assessed data]

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Acknowledgements

This study was supported by the School of Social Sciences, Humanitarian and Development Research Initiative (HADRI) and the Young and Resilient Research Centre at Western Sydney University, Australia.

Corresponding author

About the authors.

Garry John Stevens is a Senior Lecturer in the Humanitarian and Development Studies Programme at Western Sydney University. As part of the Humanitarian and Development Research Initiative (HADRI), he is involved in projects examining population preparedness for disasters and critical incidents, including occupational risk and resilience factors amongst emergency service workers, Disaster Medical Assistance Teams and humanitarian aid workers and trainees. His recent work with Aid practitioners focusses on worker self-care and well-being in the context of work-related stress. He is also involved in population mental health and epidemiology, including technology-assisted mental health-care in hospital and community settings.

Tobias Bienz holds a master’s degree in International Affairs from the University of St. Gallen. In the process of completing his degree, he consulted for the Swiss Agency for Development and Cooperation. He also taught a class on “Innovative Projects for a Sustainable Future”. Furthermore, he was selected as a Leader of Tomorrow at the 2018 St. Gallen Symposium, having successfully submitted an essay on the future of work, which was later presented in the adapted form at the Asia Pacific Humanitarian Leadership Conference in Melbourne. Tobias is also a social impact entrepreneur engaged in starting, growing and scaling social impact start-ups.

Nidhi Wali is a Senior Research Officer at the HADRI at Western Sydney University. She holds a master’s degree in Development Studies from the University of Sussex, UK and is pursuing her Doctor of Philosophy research at Western Sydney University focussing on “Child undernutrition in South Asia”. In India, she has worked with the national government, as well as with international organisations such as CARE, Public Health Resource Network and UNICEF on maternal and child health and nutrition programmes. Her present research focusses on international development, public health and migrant issues of settlement and access to services and has published across these issues.

Jenna Condie is a Senior Lecturer in Digital Society in the Social of Social Sciences and a School-based Research Fellow with the Young and Resilient Research Centre at Western Sydney University. Her interdisciplinary research traverses critical psychology, geography and technology studies. Jenna’s research is concerned with what people and places are becoming with digital technologies. Current projects focus on women’s safety, digital geographies of fear and equitable mobilities. She co-leads Travel in the Digital Age (TinDA) and Social Technologies (SoTech) research teams.

Spyros Schismenos is currently a PhD Fellow and Member of HADRI at the School of Social Sciences, Western Sydney University, Australia. Since 2016, he has been working closely with the UNESCO Chair on Conservation and Ecotourism of Riparian and Deltaic Ecosystems as the Focal Point for the Wider Region of Asia-Pacific. He is a Member of the Youth International Soil Governance Commission (YISGC) of the Food and Agriculture Organisation (FAO) of the United Nations. His research disciplines focus on Humanitarian Engineering, Disaster Management, Renewable Energy, Distance Learning, Disaster Education and Community Development.

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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.

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.

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.

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 [ PMC free article ] [ PubMed ] [ 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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Implementing Face to Face Classes: A Case for Modular Learning

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