thesis about online class

Impact of Online Classes on Students Essay

• Introduction
• Thesis Statement

Background study

• Impacts of online education

Introduction to Online Education

Online learning is one of the new innovative study methods that have been introduced in the pedagogy field. In the last few years, there has been a great shift in the training methods. Students can now learn remotely using the internet and computers.

Online learning comes in many forms and has been developing with the introduction of new technologies. Most universities, high schools, and other institutions in the world have all instituted this form of learning, and the student population in the online class is increasing fast. There has been a lot of research on the impacts of online education as compared to ordinary classroom education.

If the goal is to draw a conclusion of online education, considerable differences between the online learning environment and classroom environment should be acknowledged. In the former, teachers and students don’t meet physically as opposed to the latter, where they interact face to face. In this essay, the challenges and impact of online classes on students, teachers, and institutions involved were examined.

Thesis Statement about Online Classes

Thus, the thesis statement about online classes will be as follows:

Online learning has a positive impact on the learners, teachers, and the institution offering these courses.

Online learning or E learning is a term used to describe various learning environments that are conducted and supported by the use of computers and the internet. There are a number of definitions and terminologies that are used to describe online learning.

These include E learning, distance learning, and computer learning, among others (Anon, 2001). Distant learning is one of the terminologies used in E learning and encompasses all learning methods that are used to train students that are geographically away from the training school. Online learning, on the other hand, is used to describe all the learning methods that are supported by the Internet (Moore et al., 2011).

Another terminology that is used is E learning which most authors have described as a learning method that is supported by the use of computers, web-enabled communication, and the use of new technological tools that enhance communication (Spector, 2008). Other terminologies that are used to describe this form of online learning are virtual learning, collaborative learning, web-based learning, and computer-supported collaborative learning (Conrad, 2006).

Impacts of Online Classes on Students

Various studies and articles document the merits, demerits, and challenges of online studies. These studies show that online study is far beneficial to the students, teachers, and the institution in general and that the current challenges can be overcome through technological advancement and increasing efficiency of the learning process.

One of the key advantages of online learning is the ability of students to study in their own comfort. For a long time, students had to leave their comfort areas and attend lectures. This change in environment causes a lack of concentration in students. In contrast, E-learning enables the students to choose the best environment for study, and this promotes their ability to understand. As a result, students enjoy the learning process as compared to conventional classroom learning.

Another benefit is time and cost savings. Online students are able to study at home, and this saves them travel and accommodation costs. This is in contrast with the classroom environment, where learners have to pay for transport and accommodation costs as well as any other costs associated with the learning process.

Online study has been found to reduce the workload on the tutors. Most of the online notes and books are availed to the students, and this reduces the teacher’s workload. Due to the availability of teaching materials online, tutors are not required to search for materials. Teachers usually prepare lessons, and this reduces the task of training students over and over again.

Accessibility to learning materials is another benefit of online learning. Students participating in online study have unlimited access to learning materials, which gives them the ability to study effectively and efficiently. On the other hand, students in the classroom environment have to take notes as the lecture progress, and these notes may not be accurate as compared to the materials uploaded on the websites.

Unlimited resources are another advantage of online study. Traditionally, learning institutions were limited in the number of students that could study in the classroom environment. The limitations of facilities such as lecture theaters and teachers limited student enrollment in schools (Burgess & Russell, 2003).

However, with the advent of online studies, physical limitations imposed by classrooms, tutors, and other resources have been eliminated. A vast number of students can now study in the same institution and be able to access the learning materials online. The use of online media for training enables a vast number of students to access materials online, and this promotes the learning process.

Promoting online study has been found by most researchers to open the students to vast resources that are found on the internet. Most of the students in the classroom environment rely on the tutors’ notes and explanations for them to understand a given concept.

However, students using the web to study most of the time are likely to be exposed to the vast online educational resources that are available. This results in the students gaining a better understanding of the concept as opposed to those in the classroom environment (Berge & Giles, 2008).

An online study environment allows tutors to update their notes and other materials much faster as compared to the classroom environment. This ensures that the students receive up-to-date information on a given study area.

One of the main benefits of E-learning to institutions is the ability to provide training to a large number of students located in any corner of the world. These students are charged training fees, and this increases the money available to the institution. This extra income can be used to develop new educational facilities, and these will promote education further (Gilli et al., 2002).

Despite the many advantages that online study has in transforming the learning process, there are some challenges imposed by the method. One of the challenges is the technological limitations of the current computers, which affect the quality of the learning materials and the learning process in general.

Low download speed and slow internet connectivity affect the availability of learning materials. This problem is, however, been reduced through the application of new software and hardware elements that have high access speeds. This makes it easier to download learning materials and applications. As computing power increases, better and faster computers are being unveiled, and these will enable better access to online study facilities.

Another disadvantage of online learning as compared to the classroom environment is the lack of feedback from the students. In the classroom environment, students listen to the lecture and ask the tutors questions and clarifications any issues they didn’t understand. In the online environment, the response by the teacher may not be immediate, and students who don’t understand a given concept may find it hard to liaise with the teachers.

The problem is, however, been circumvented by the use of simple explanation methods, slideshows, and encouraging discussion forums between the teachers and students. In the discussion forums, students who don’t understand a concept can leave a comment or question, which will be answered by the tutor later.

Like any other form of learning, online studies have a number of benefits and challenges. It is, therefore, not logical to discredit online learning due to the negative impacts of this training method. Furthermore, the benefits of e-learning far outweigh the challenges.

Conclusion about Online Education

In culmination, a comparative study between classroom study and online study was carried out. The study was done by examining the findings recorded in books and journals on the applicability of online learning to students. The study revealed that online learning has many benefits as compared to conventional learning in the classroom environment.

Though online learning has several challenges, such as a lack of feedback from students and a lack of the proper technology to effectively conduct online learning, these limitations can be overcome by upgrading the E-Leaning systems and the use of online discussion forums and new web-based software.

In conclusion, online learning is beneficial to the students, tutors, and the institution offering these courses. I would therefore recommend that online learning be implemented in all learning institutions, and research on how to improve this learning process should be carried out.

Anon, C. (2001). E-learning is taking off in Europe. Industrial and Commercial Training , 33 (7), 280-282.

Berge, Z., & Giles, L. (2008). Implementing and sustaining e-learning in the workplace. International Journal of Web-Based Learning and Teaching Technologies , 3(3), 44-53.

Burgess, J. & Russell, J. (2003).The effectiveness of distance learning initiatives in organizations. Journal of Vocational Behaviour , 63 (2),289-303.

Conrad, D. (2006). E-Learning and social change, Perspectives on higher education in the digital age . New York: Nova Science Publishers.

Gilli, R., Pulcini, M., Tonchia, S. & Zavagno, M. (2002), E-learning: A strategic Instrument. International Journal of Business Performance Management , 4 (1), 2-4.

Moore, J. L., Camille, D. & Galyen, K. (2011). E-Learning, online learning and distance learning environments: Are they the same? Internet and Higher Education, 14(1), 129-135.

Spector, J., Merrill, M., Merrienboer, J. & Driscoll, M. P. (2008). Handbook of research on educational communications and technology (3rd ed.), New York: Lawrence Erlbaum Associates.

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

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

Cite this article

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

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

Avoid common mistakes on your manuscript.

1 Introduction

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

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

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

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

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

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

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

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

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

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

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

2 Theoretical framework

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

Proposed Model

3 Hypotheses development

3.1 quality of the instructor and satisfaction of the students.

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

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

3.2 Course design and satisfaction of students

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

H2: Course design positively affects the satisfaction of students.

3.3 Prompt feedback and satisfaction of students

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

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

3.4 Expectations and satisfaction of students

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

H4: Expectations of the students positively affects the satisfaction.

3.5 Satisfaction and performance of the students

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

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

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

3.6 Satisfaction as mediator

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

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

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

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

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

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

4.1 Participants

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

4.2 Materials

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

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

4.4 Procedure

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

5.1 Exploratory factor analysis (EFA)

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

5.2 Measurement model

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

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

5.3 Structural model

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

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

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

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

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

6 Discussion

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

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

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

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

7 Implications of the study

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

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

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

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Received : 07 December 2020

Accepted : 22 March 2021

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Issue Date : November 2021

DOI : https://doi.org/10.1007/s10639-021-10523-1

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Original research article, a comparative analysis of student performance in an online vs. face-to-face environmental science course from 2009 to 2016.

• Department of Biology, Fort Valley State University, Fort Valley, GA, United States

A growing number of students are now opting for online classes. They find the traditional classroom modality restrictive, inflexible, and impractical. In this age of technological advancement, schools can now provide effective classroom teaching via the Web. This shift in pedagogical medium is forcing academic institutions to rethink how they want to deliver their course content. The overarching purpose of this research was to determine which teaching method proved more effective over the 8-year period. The scores of 548 students, 401 traditional students and 147 online students, in an environmental science class were used to determine which instructional modality generated better student performance. In addition to the overarching objective, we also examined score variabilities between genders and classifications to determine if teaching modality had a greater impact on specific groups. No significant difference in student performance between online and face-to-face (F2F) learners overall, with respect to gender, or with respect to class rank were found. These data demonstrate the ability to similarly translate environmental science concepts for non-STEM majors in both traditional and online platforms irrespective of gender or class rank. A potential exists for increasing the number of non-STEM majors engaged in citizen science using the flexibility of online learning to teach environmental science core concepts.

Introduction

The advent of online education has made it possible for students with busy lives and limited flexibility to obtain a quality education. As opposed to traditional classroom teaching, Web-based instruction has made it possible to offer classes worldwide through a single Internet connection. Although it boasts several advantages over traditional education, online instruction still has its drawbacks, including limited communal synergies. Still, online education seems to be the path many students are taking to secure a degree.

This study compared the effectiveness of online vs. traditional instruction in an environmental studies class. Using a single indicator, we attempted to see if student performance was effected by instructional medium. This study sought to compare online and F2F teaching on three levels—pure modality, gender, and class rank. Through these comparisons, we investigated whether one teaching modality was significantly more effective than the other. Although there were limitations to the study, this examination was conducted to provide us with additional measures to determine if students performed better in one environment over another ( Mozes-Carmel and Gold, 2009 ).

The methods, procedures, and operationalization tools used in this assessment can be expanded upon in future quantitative, qualitative, and mixed method designs to further analyze this topic. Moreover, the results of this study serve as a backbone for future meta-analytical studies.

Origins of Online Education

Computer-assisted instruction is changing the pedagogical landscape as an increasing number of students are seeking online education. Colleges and universities are now touting the efficiencies of Web-based education and are rapidly implementing online classes to meet student needs worldwide. One study reported “increases in the number of online courses given by universities have been quite dramatic over the last couple of years” ( Lundberg et al., 2008 ). Think tanks are also disseminating statistics on Web-based instruction. “In 2010, the Sloan Consortium found a 17% increase in online students from the years before, beating the 12% increase from the previous year” ( Keramidas, 2012 ).

Contrary to popular belief, online education is not a new phenomenon. The first correspondence and distance learning educational programs were initiated in the mid-1800s by the University of London. This model of educational learning was dependent on the postal service and therefore wasn't seen in American until the later Nineteenth century. It was in 1873 when what is considered the first official correspondence educational program was established in Boston, Massachusetts known as the “Society to Encourage Home Studies.” Since then, non-traditional study has grown into what it is today considered a more viable online instructional modality. Technological advancement indubitably helped improve the speed and accessibility of distance learning courses; now students worldwide could attend classes from the comfort of their own homes.

Qualities of Online and Traditional Face to Face (F2F) Classroom Education

Online and traditional education share many qualities. Students are still required to attend class, learn the material, submit assignments, and complete group projects. While teachers, still have to design curriculums, maximize instructional quality, answer class questions, motivate students to learn, and grade assignments. Despite these basic similarities, there are many differences between the two modalities. Traditionally, classroom instruction is known to be teacher-centered and requires passive learning by the student, while online instruction is often student-centered and requires active learning.

In teacher-centered, or passive learning, the instructor usually controls classroom dynamics. The teacher lectures and comments, while students listen, take notes, and ask questions. In student-centered, or active learning, the students usually determine classroom dynamics as they independently analyze the information, construct questions, and ask the instructor for clarification. In this scenario, the teacher, not the student, is listening, formulating, and responding ( Salcedo, 2010 ).

In education, change comes with questions. Despite all current reports championing online education, researchers are still questioning its efficacy. Research is still being conducted on the effectiveness of computer-assisted teaching. Cost-benefit analysis, student experience, and student performance are now being carefully considered when determining whether online education is a viable substitute for classroom teaching. This decision process will most probably carry into the future as technology improves and as students demand better learning experiences.

Thus far, “literature on the efficacy of online courses is expansive and divided” ( Driscoll et al., 2012 ). Some studies favor traditional classroom instruction, stating “online learners will quit more easily” and “online learning can lack feedback for both students and instructors” ( Atchley et al., 2013 ). Because of these shortcomings, student retention, satisfaction, and performance can be compromised. Like traditional teaching, distance learning also has its apologists who aver online education produces students who perform as well or better than their traditional classroom counterparts ( Westhuis et al., 2006 ).

The advantages and disadvantages of both instructional modalities need to be fully fleshed out and examined to truly determine which medium generates better student performance. Both modalities have been proven to be relatively effective, but, as mentioned earlier, the question to be asked is if one is truly better than the other.

Student Need for Online Education

With technological advancement, learners now want quality programs they can access from anywhere and at any time. Because of these demands, online education has become a viable, alluring option to business professionals, stay-at home-parents, and other similar populations. In addition to flexibility and access, multiple other face value benefits, including program choice and time efficiency, have increased the attractiveness of distance learning ( Wladis et al., 2015 ).

First, prospective students want to be able to receive a quality education without having to sacrifice work time, family time, and travel expense. Instead of having to be at a specific location at a specific time, online educational students have the freedom to communicate with instructors, address classmates, study materials, and complete assignments from any Internet-accessible point ( Richardson and Swan, 2003 ). This type of flexibility grants students much-needed mobility and, in turn, helps make the educational process more enticing. According to Lundberg et al. (2008) “the student may prefer to take an online course or a complete online-based degree program as online courses offer more flexible study hours; for example, a student who has a job could attend the virtual class watching instructional film and streaming videos of lectures after working hours.”

Moreover, more study time can lead to better class performance—more chapters read, better quality papers, and more group project time. Studies on the relationship between study time and performance are limited; however, it is often assumed the online student will use any surplus time to improve grades ( Bigelow, 2009 ). It is crucial to mention the link between flexibility and student performance as grades are the lone performance indicator of this research.

Second, online education also offers more program choices. With traditional classroom study, students are forced to take courses only at universities within feasible driving distance or move. Web-based instruction, on the other hand, grants students electronic access to multiple universities and course offerings ( Salcedo, 2010 ). Therefore, students who were once limited to a few colleges within their immediate area can now access several colleges worldwide from a single convenient location.

Third, with online teaching, students who usually don't participate in class may now voice their opinions and concerns. As they are not in a classroom setting, quieter students may feel more comfortable partaking in class dialogue without being recognized or judged. This, in turn, may increase average class scores ( Driscoll et al., 2012 ).

Benefits of Face-to-Face (F2F) Education via Traditional Classroom Instruction

The other modality, classroom teaching, is a well-established instructional medium in which teaching style and structure have been refined over several centuries. Face-to-face instruction has numerous benefits not found in its online counterpart ( Xu and Jaggars, 2016 ).

First and, perhaps most importantly, classroom instruction is extremely dynamic. Traditional classroom teaching provides real-time face-to-face instruction and sparks innovative questions. It also allows for immediate teacher response and more flexible content delivery. Online instruction dampens the learning process because students must limit their questions to blurbs, then grant the teacher and fellow classmates time to respond ( Salcedo, 2010 ). Over time, however, online teaching will probably improve, enhancing classroom dynamics and bringing students face-to face with their peers/instructors. However, for now, face-to-face instruction provides dynamic learning attributes not found in Web-based teaching ( Kemp and Grieve, 2014 ).

Second, traditional classroom learning is a well-established modality. Some students are opposed to change and view online instruction negatively. These students may be technophobes, more comfortable with sitting in a classroom taking notes than sitting at a computer absorbing data. Other students may value face-to-face interaction, pre and post-class discussions, communal learning, and organic student-teacher bonding ( Roval and Jordan, 2004 ). They may see the Internet as an impediment to learning. If not comfortable with the instructional medium, some students may shun classroom activities; their grades might slip and their educational interest might vanish. Students, however, may eventually adapt to online education. With more universities employing computer-based training, students may be forced to take only Web-based courses. Albeit true, this doesn't eliminate the fact some students prefer classroom intimacy.

Third, face-to-face instruction doesn't rely upon networked systems. In online learning, the student is dependent upon access to an unimpeded Internet connection. If technical problems occur, online students may not be able to communicate, submit assignments, or access study material. This problem, in turn, may frustrate the student, hinder performance, and discourage learning.

Fourth, campus education provides students with both accredited staff and research libraries. Students can rely upon administrators to aid in course selection and provide professorial recommendations. Library technicians can help learners edit their papers, locate valuable study material, and improve study habits. Research libraries may provide materials not accessible by computer. In all, the traditional classroom experience gives students important auxiliary tools to maximize classroom performance.

Fifth, traditional classroom degrees trump online educational degrees in terms of hiring preferences. Many academic and professional organizations do not consider online degrees on par with campus-based degrees ( Columbaro and Monaghan, 2009 ). Often, prospective hiring bodies think Web-based education is a watered-down, simpler means of attaining a degree, often citing poor curriculums, unsupervised exams, and lenient homework assignments as detriments to the learning process.

Finally, research shows online students are more likely to quit class if they do not like the instructor, the format, or the feedback. Because they work independently, relying almost wholly upon self-motivation and self-direction, online learners may be more inclined to withdraw from class if they do not get immediate results.

The classroom setting provides more motivation, encouragement, and direction. Even if a student wanted to quit during the first few weeks of class, he/she may be deterred by the instructor and fellow students. F2F instructors may be able to adjust the structure and teaching style of the class to improve student retention ( Kemp and Grieve, 2014 ). With online teaching, instructors are limited to electronic correspondence and may not pick-up on verbal and non-verbal cues.

Both F2F and online teaching have their pros and cons. More studies comparing the two modalities to achieve specific learning outcomes in participating learner populations are required before well-informed decisions can be made. This study examined the two modalities over eight (8) years on three different levels. Based on the aforementioned information, the following research questions resulted.

RQ1: Are there significant differences in academic performance between online and F2F students enrolled in an environmental science course?

RQ2: Are there gender differences between online and F2F student performance in an environmental science course?

RQ3: Are there significant differences between the performance of online and F2F students in an environmental science course with respect to class rank?

The results of this study are intended to edify teachers, administrators, and policymakers on which medium may work best.

Methodology

Participants.

The study sample consisted of 548 FVSU students who completed the Environmental Science class between 2009 and 2016. The final course grades of the participants served as the primary comparative factor in assessing performance differences between online and F2F instruction. Of the 548 total participants, 147 were online students while 401 were traditional students. This disparity was considered a limitation of the study. Of the 548 total students, 246 were male, while 302 were female. The study also used students from all four class ranks. There were 187 freshmen, 184 sophomores, 76 juniors, and 101 seniors. This was a convenience, non-probability sample so the composition of the study set was left to the discretion of the instructor. No special preferences or weights were given to students based upon gender or rank. Each student was considered a single, discrete entity or statistic.

All sections of the course were taught by a full-time biology professor at FVSU. The professor had over 10 years teaching experience in both classroom and F2F modalities. The professor was considered an outstanding tenured instructor with strong communication and management skills.

The F2F class met twice weekly in an on-campus classroom. Each class lasted 1 h and 15 min. The online class covered the same material as the F2F class, but was done wholly on-line using the Desire to Learn (D2L) e-learning system. Online students were expected to spend as much time studying as their F2F counterparts; however, no tracking measure was implemented to gauge e-learning study time. The professor combined textbook learning, lecture and class discussion, collaborative projects, and assessment tasks to engage students in the learning process.

This study did not differentiate between part-time and full-time students. Therefore, many part-time students may have been included in this study. This study also did not differentiate between students registered primarily at FVSU or at another institution. Therefore, many students included in this study may have used FVSU as an auxiliary institution to complete their environmental science class requirement.

Test Instruments

In this study, student performance was operationalized by final course grades. The final course grade was derived from test, homework, class participation, and research project scores. The four aforementioned assessments were valid and relevant; they were useful in gauging student ability and generating objective performance measurements. The final grades were converted from numerical scores to traditional GPA letters.

Data Collection Procedures

The sample 548 student grades were obtained from FVSU's Office of Institutional Research Planning and Effectiveness (OIRPE). The OIRPE released the grades to the instructor with the expectation the instructor would maintain confidentiality and not disclose said information to third parties. After the data was obtained, the instructor analyzed and processed the data though SPSS software to calculate specific values. These converted values were subsequently used to draw conclusions and validate the hypothesis.

Summary of the Results: The chi-square analysis showed no significant difference in student performance between online and face-to-face (F2F) learners [χ 2 (4, N = 548) = 6.531, p > 0.05]. The independent sample t -test showed no significant difference in student performance between online and F2F learners with respect to gender [ t (145) = 1.42, p = 0.122]. The 2-way ANOVA showed no significant difference in student performance between online and F2F learners with respect to class rank ( Girard et al., 2016 ).

Research question #1 was to determine if there was a statistically significant difference between the academic performance of online and F2F students.

Research Question 1

The first research question investigated if there was a difference in student performance between F2F and online learners.

To investigate the first research question, we used a traditional chi-square method to analyze the data. The chi-square analysis is particularly useful for this type of comparison because it allows us to determine if the relationship between teaching modality and performance in our sample set can be extended to the larger population. The chi-square method provides us with a numerical result which can be used to determine if there is a statistically significant difference between the two groups.

Table 1 shows us the mean and SD for modality and for gender. It is a general breakdown of numbers to visually elucidate any differences between scores and deviations. The mean GPA for both modalities is similar with F2F learners scoring a 69.35 and online learners scoring a 68.64. Both groups had fairly similar SDs. A stronger difference can be seen between the GPAs earned by men and women. Men had a 3.23 mean GPA while women had a 2.9 mean GPA. The SDs for both groups were almost identical. Even though the 0.33 numerical difference may look fairly insignificant, it must be noted that a 3.23 is approximately a B+ while a 2.9 is approximately a B. Given a categorical range of only A to F, a plus differential can be considered significant.

Table 1 . Means and standard deviations for 8 semester- “Environmental Science data set.”

The mean grade for men in the environmental online classes ( M = 3.23, N = 246, SD = 1.19) was higher than the mean grade for women in the classes ( M = 2.9, N = 302, SD = 1.20) (see Table 1 ).

First, a chi-square analysis was performed using SPSS to determine if there was a statistically significant difference in grade distribution between online and F2F students. Students enrolled in the F2F class had the highest percentage of A's (63.60%) as compared to online students (36.40%). Table 2 displays grade distribution by course delivery modality. The difference in student performance was statistically significant, χ 2 (4, N = 548) = 6.531, p > 0.05. Table 3 shows the gender difference on student performance between online and F2F students.

Table 2 . Contingency table for student's academic performance ( N = 548).

Table 3 . Gender * performance crosstabulation.

Table 2 shows us the performance measures of online and F2F students by grade category. As can be seen, F2F students generated the highest performance numbers for each grade category. However, this disparity was mostly due to a higher number of F2F students in the study. There were 401 F2F students as opposed to just 147 online students. When viewing grades with respect to modality, there are smaller percentage differences between respective learners ( Tanyel and Griffin, 2014 ). For example, F2F learners earned 28 As (63.60% of total A's earned) while online learners earned 16 As (36.40% of total A's earned). However, when viewing the A grade with respect to total learners in each modality, it can be seen that 28 of the 401 F2F students (6.9%) earned As as compared to 16 of 147 (10.9%) online learners. In this case, online learners scored relatively higher in this grade category. The latter measure (grade total as a percent of modality total) is a better reflection of respective performance levels.

Given a critical value of 7.7 and a d.f. of 4, we were able to generate a chi-squared measure of 6.531. The correlating p -value of 0.163 was greater than our p -value significance level of 0.05. We, therefore, had to accept the null hypothesis and reject the alternative hypothesis. There is no statistically significant difference between the two groups in terms of performance scores.

Research Question 2

The second research question was posed to evaluate if there was a difference between online and F2F varied with gender. Does online and F2F student performance vary with respect to gender? Table 3 shows the gender difference on student performance between online and face to face students. We used chi-square test to determine if there were differences in online and F2F student performance with respect to gender. The chi-square test with alpha equal to 0.05 as criterion for significance. The chi-square result shows that there is no statistically significant difference between men and women in terms of performance.

Research Question 3

The third research question tried to determine if there was a difference between online and F2F varied with respect to class rank. Does online and F2F student performance vary with respect to class rank?

Table 4 shows the mean scores and standard deviations of freshman, sophomore, and junior and senior students for both online and F2F student performance. To test the third hypothesis, we used a two-way ANOVA. The ANOVA is a useful appraisal tool for this particular hypothesis as it tests the differences between multiple means. Instead of testing specific differences, the ANOVA generates a much broader picture of average differences. As can be seen in Table 4 , the ANOVA test for this particular hypothesis states there is no significant difference between online and F2F learners with respect to class rank. Therefore, we must accept the null hypothesis and reject the alternative hypothesis.

Table 4 . Descriptive analysis of student performance by class rankings gender.

The results of the ANOVA show there is no significant difference in performance between online and F2F students with respect to class rank. Results of ANOVA is presented in Table 5 .

Table 5 . Analysis of variance (ANOVA) for online and F2F of class rankings.

As can be seen in Table 4 , the ANOVA test for this particular hypothesis states there is no significant difference between online and F2F learners with respect to class rank. Therefore, we must accept the null hypothesis and reject the alternative hypothesis.

Discussion and Social Implications

The results of the study show there is no significant difference in performance between online and traditional classroom students with respect to modality, gender, or class rank in a science concepts course for non-STEM majors. Although there were sample size issues and study limitations, this assessment shows both online learners and classroom learners perform at the same level. This conclusion indicates teaching modality may not matter as much as other factors. Given the relatively sparse data on pedagogical modality comparison given specific student population characteristics, this study could be considered innovative. In the current literature, we have not found a study of this nature comparing online and F2F non-STEM majors with respect to three separate factors—medium, gender, and class rank—and the ability to learn science concepts and achieve learning outcomes. Previous studies have compared traditional classroom learning vs. F2F learning for other factors (including specific courses, costs, qualitative analysis, etcetera, but rarely regarding outcomes relevant to population characteristics of learning for a specific science concepts course over many years) ( Liu, 2005 ).

In a study evaluating the transformation of a graduate level course for teachers, academic quality of the online course and learning outcomes were evaluated. The study evaluated the ability of course instructors to design the course for online delivery and develop various interactive multimedia models at a cost-savings to the respective university. The online learning platform proved effective in translating information where tested students successfully achieved learning outcomes comparable to students taking the F2F course ( Herman and Banister, 2007 ).

Another study evaluated the similarities and differences in F2F and online learning in a non-STEM course, “Foundations of American Education” and overall course satisfaction by students enrolled in either of the two modalities. F2F and online course satisfaction was qualitatively and quantitative analyzed. However, in analyzing online and F2F course feedback using quantitative feedback, online course satisfaction was less than F2F satisfaction. When qualitative data was used, course satisfaction was similar between modalities ( Werhner, 2010 ). The course satisfaction data and feedback was used to suggest a number of posits for effective online learning in the specific course. The researcher concluded that there was no difference in the learning success of students enrolled in the online vs. F2F course, stating that “in terms of learning, students who apply themselves diligently should be successful in either format” ( Dell et al., 2010 ). The author's conclusion presumes that the “issues surrounding class size are under control and that the instructor has a course load that makes the intensity of the online course workload feasible” where the authors conclude that the workload for online courses is more than for F2F courses ( Stern, 2004 ).

In “A Meta-Analysis of Three Types of Interaction Treatments in Distance Education,” Bernard et al. (2009) conducted a meta-analysis evaluating three types of instructional and/or media conditions designed into distance education (DE) courses known as interaction treatments (ITs)—student–student (SS), student–teacher (ST), or student–content (SC) interactions—to other DE instructional/interaction treatments. The researchers found that a strong association existed between the integration of these ITs into distance education courses and achievement compared with blended or F2F modalities of learning. The authors speculated that this was due to increased cognitive engagement based in these three interaction treatments ( Larson and Sung, 2009 ).

Other studies evaluating students' preferences (but not efficacy) for online vs. F2F learning found that students prefer online learning when it was offered, depending on course topic, and online course technology platform ( Ary and Brune, 2011 ). F2F learning was preferred when courses were offered late morning or early afternoon 2–3 days/week. A significant preference for online learning resulted across all undergraduate course topics (American history and government, humanities, natural sciences, social, and behavioral sciences, diversity, and international dimension) except English composition and oral communication. A preference for analytical and quantitative thought courses was also expressed by students, though not with statistically significant results ( Mann and Henneberry, 2014 ). In this research study, we looked at three hypothesis comparing online and F2F learning. In each case, the null hypothesis was accepted. Therefore, at no level of examination did we find a significant difference between online and F2F learners. This finding is important because it tells us traditional-style teaching with its heavy emphasis on interpersonal classroom dynamics may 1 day be replaced by online instruction. According to Daymont and Blau (2008) online learners, regardless of gender or class rank, learn as much from electronic interaction as they do from personal interaction. Kemp and Grieve (2014) also found that both online and F2F learning for psychology students led to similar academic performance. Given the cost efficiencies and flexibility of online education, Web-based instructional systems may rapidly rise.

A number of studies support the economic benefits of online vs. F2F learning, despite differences in social constructs and educational support provided by governments. In a study by Li and Chen (2012) higher education institutions benefit the most from two of four outputs—research outputs and distance education—with teaching via distance education at both the undergraduate and graduate levels more profitable than F2F teaching at higher education institutions in China. Zhang and Worthington (2017) reported an increasing cost benefit for the use of distance education over F2F instruction as seen at 37 Australian public universities over 9 years from 2003 to 2012. Maloney et al. (2015) and Kemp and Grieve (2014) also found significant savings in higher education when using online learning platforms vs. F2F learning. In the West, the cost efficiency of online learning has been demonstrated by several research studies ( Craig, 2015 ). Studies by Agasisti and Johnes (2015) and Bartley and Golek (2004) both found the cost benefits of online learning significantly greater than that of F2F learning at U.S. institutions.

Knowing there is no significant difference in student performance between the two mediums, institutions of higher education may make the gradual shift away from traditional instruction; they may implement Web-based teaching to capture a larger worldwide audience. If administered correctly, this shift to Web-based teaching could lead to a larger buyer population, more cost efficiencies, and more university revenue.

The social implications of this study should be touted; however, several concerns regarding generalizability need to be taken into account. First, this study focused solely on students from an environmental studies class for non-STEM majors. The ability to effectively prepare students for scientific professions without hands-on experimentation has been contended. As a course that functions to communicate scientific concepts, but does not require a laboratory based component, these results may not translate into similar performance of students in an online STEM course for STEM majors or an online course that has an online laboratory based co-requisite when compared to students taking traditional STEM courses for STEM majors. There are few studies that suggest the landscape may be changing with the ability to effectively train students in STEM core concepts via online learning. Biel and Brame (2016) reported successfully translating the academic success of F2F undergraduate biology courses to online biology courses. However, researchers reported that of the large-scale courses analyzed, two F2F sections outperformed students in online sections, and three found no significant difference. A study by Beale et al. (2014) comparing F2F learning with hybrid learning in an embryology course found no difference in overall student performance. Additionally, the bottom quartile of students showed no differential effect of the delivery method on examination scores. Further, a study from Lorenzo-Alvarez et al. (2019) found that radiology education in an online learning platform resulted in similar academic outcomes as F2F learning. Larger scale research is needed to determine the effectiveness of STEM online learning and outcomes assessments, including workforce development results.

In our research study, it is possible the study participants may have been more knowledgeable about environmental science than about other subjects. Therefore, it should be noted this study focused solely on students taking this one particular class. Given the results, this course presents a unique potential for increasing the number of non-STEM majors engaged in citizen science using the flexibility of online learning to teach environmental science core concepts.

Second, the operationalization measure of “grade” or “score” to determine performance level may be lacking in scope and depth. The grades received in a class may not necessarily show actual ability, especially if the weights were adjusted to heavily favor group tasks and writing projects. Other performance indicators may be better suited to properly access student performance. A single exam containing both multiple choice and essay questions may be a better operationalization indicator of student performance. This type of indicator will provide both a quantitative and qualitative measure of subject matter comprehension.

Third, the nature of the student sample must be further dissected. It is possible the online students in this study may have had more time than their counterparts to learn the material and generate better grades ( Summers et al., 2005 ). The inverse holds true, as well. Because this was a convenience non-probability sampling, the chances of actually getting a fair cross section of the student population were limited. In future studies, greater emphasis must be placed on selecting proper study participants, those who truly reflect proportions, types, and skill levels.

This study was relevant because it addressed an important educational topic; it compared two student groups on multiple levels using a single operationalized performance measure. More studies, however, of this nature need to be conducted before truly positing that online and F2F teaching generate the same results. Future studies need to eliminate spurious causal relationships and increase generalizability. This will maximize the chances of generating a definitive, untainted results. This scientific inquiry and comparison into online and traditional teaching will undoubtedly garner more attention in the coming years.

Our study compared learning via F2F vs. online learning modalities in teaching an environmental science course additionally evaluating factors of gender and class rank. These data demonstrate the ability to similarly translate environmental science concepts for non-STEM majors in both traditional and online platforms irrespective of gender or class rank. The social implications of this finding are important for advancing access to and learning of scientific concepts by the general population, as many institutions of higher education allow an online course to be taken without enrolling in a degree program. Thus, the potential exists for increasing the number of non-STEM majors engaged in citizen science using the flexibility of online learning to teach environmental science core concepts.

Limitations of the Study

The limitations of the study centered around the nature of the sample group, student skills/abilities, and student familiarity with online instruction. First, because this was a convenience, non-probability sample, the independent variables were not adjusted for real-world accuracy. Second, student intelligence and skill level were not taken into consideration when separating out comparison groups. There exists the possibility that the F2F learners in this study may have been more capable than the online students and vice versa. This limitation also applies to gender and class rank differences ( Friday et al., 2006 ). Finally, there may have been ease of familiarity issues between the two sets of learners. Experienced traditional classroom students now taking Web-based courses may be daunted by the technical aspect of the modality. They may not have had the necessary preparation or experience to efficiently e-learn, thus leading to lowered scores ( Helms, 2014 ). In addition to comparing online and F2F instructional efficacy, future research should also analyze blended teaching methods for the effectiveness of courses for non-STEM majors to impart basic STEM concepts and see if the blended style is more effective than any one pure style.

Data Availability Statement

The datasets generated for this study are available on request to the corresponding author.

Ethics Statement

The studies involving human participants were reviewed and approved by Fort Valley State University Human Subjects Institutional Review Board. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author Contributions

JP provided substantial contributions to the conception of the work, acquisition and analysis of data for the work, and is the corresponding author on this paper who agrees to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. FJ provided substantial contributions to the design of the work, interpretation of the data for the work, and revised it critically for intellectual content.

This research was supported in part by funding from the National Science Foundation, Awards #1649717, 1842510, Ñ900572, and 1939739 to FJ.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors would like to thank the reviewers for their detailed comments and feedback that assisted in the revising of our original manuscript.

Agasisti, T., and Johnes, G. (2015). Efficiency, costs, rankings and heterogeneity: the case of US higher education. Stud. High. Educ. 40, 60–82. doi: 10.1080/03075079.2013.818644

CrossRef Full Text | Google Scholar

Ary, E. J., and Brune, C. W. (2011). A comparison of student learning outcomes in traditional and online personal finance courses. MERLOT J. Online Learn. Teach. 7, 465–474.

Google Scholar

Atchley, W., Wingenbach, G., and Akers, C. (2013). Comparison of course completion and student performance through online and traditional courses. Int. Rev. Res. Open Dist. Learn. 14, 104–116. doi: 10.19173/irrodl.v14i4.1461

Bartley, S. J., and Golek, J. H. (2004). Evaluating the cost effectiveness of online and face-to-face instruction. Educ. Technol. Soc. 7, 167–175.

Beale, E. G., Tarwater, P. M., and Lee, V. H. (2014). A retrospective look at replacing face-to-face embryology instruction with online lectures in a human anatomy course. Am. Assoc. Anat. 7, 234–241. doi: 10.1002/ase.1396

PubMed Abstract | CrossRef Full Text | Google Scholar

Bernard, R. M., Abrami, P. C., Borokhovski, E., Wade, C. A., Tamim, R. M., Surkesh, M. A., et al. (2009). A meta-analysis of three types of interaction treatments in distance education. Rev. Educ. Res. 79, 1243–1289. doi: 10.3102/0034654309333844

Biel, R., and Brame, C. J. (2016). Traditional versus online biology courses: connecting course design and student learning in an online setting. J. Microbiol. Biol. Educ. 17, 417–422. doi: 10.1128/jmbe.v17i3.1157

Bigelow, C. A. (2009). Comparing student performance in an online versus a face to face introductory turfgrass science course-a case study. NACTA J. 53, 1–7.

Columbaro, N. L., and Monaghan, C. H. (2009). Employer perceptions of online degrees: a literature review. Online J. Dist. Learn. Administr. 12.

Craig, R. (2015). A Brief History (and Future) of Online Degrees. Forbes/Education . Available online at: https://www.forbes.com/sites/ryancraig/2015/06/23/a-brief-history-and-future-of-online-degrees/#e41a4448d9a8

Daymont, T., and Blau, G. (2008). Student performance in online and traditional sections of an undergraduate management course. J. Behav. Appl. Manag. 9, 275–294.

Dell, C. A., Low, C., and Wilker, J. F. (2010). Comparing student achievement in online and face-to-face class formats. J. Online Learn. Teach. Long Beach 6, 30–42.

Driscoll, A., Jicha, K., Hunt, A. N., Tichavsky, L., and Thompson, G. (2012). Can online courses deliver in-class results? A comparison of student performance and satisfaction in an online versus a face-to-face introductory sociology course. Am. Sociol. Assoc . 40, 312–313. doi: 10.1177/0092055X12446624

Friday, E., Shawnta, S., Green, A. L., and Hill, A. Y. (2006). A multi-semester comparison of student performance between multiple traditional and online sections of two management courses. J. Behav. Appl. Manag. 8, 66–81.

Girard, J. P., Yerby, J., and Floyd, K. (2016). Knowledge retention in capstone experiences: an analysis of online and face-to-face courses. Knowl. Manag. ELearn. 8, 528–539. doi: 10.34105/j.kmel.2016.08.033

Helms, J. L. (2014). Comparing student performance in online and face-to-face delivery modalities. J. Asynchr. Learn. Netw. 18, 1–14. doi: 10.24059/olj.v18i1.348

Herman, T., and Banister, S. (2007). Face-to-face versus online coursework: a comparison of costs and learning outcomes. Contemp. Issues Technol. Teach. Educ. 7, 318–326.

Kemp, N., and Grieve, R. (2014). Face-to-Face or face-to-screen? Undergraduates' opinions and test performance in classroom vs. online learning. Front. Psychol. 5:1278. doi: 10.3389/fpsyg.2014.01278

Keramidas, C. G. (2012). Are undergraduate students ready for online learning? A comparison of online and face-to-face sections of a course. Rural Special Educ. Q . 31, 25–39. doi: 10.1177/875687051203100405

Larson, D.K., and Sung, C. (2009). Comparing student performance: online versus blended versus face-to-face. J. Asynchr. Learn. Netw. 13, 31–42. doi: 10.24059/olj.v13i1.1675

Li, F., and Chen, X. (2012). Economies of scope in distance education: the case of Chinese Research Universities. Int. Rev. Res. Open Distrib. Learn. 13, 117–131.

Liu, Y. (2005). Effects of online instruction vs. traditional instruction on student's learning. Int. J. Instruct. Technol. Dist. Learn. 2, 57–64.

Lorenzo-Alvarez, R., Rudolphi-Solero, T., Ruiz-Gomez, M. J., and Sendra-Portero, F. (2019). Medical student education for abdominal radiographs in a 3D virtual classroom versus traditional classroom: a randomized controlled trial. Am. J. Roentgenol. 213, 644–650. doi: 10.2214/AJR.19.21131

Lundberg, J., Castillo-Merino, D., and Dahmani, M. (2008). Do online students perform better than face-to-face students? Reflections and a short review of some Empirical Findings. Rev. Univ. Soc. Conocim . 5, 35–44. doi: 10.7238/rusc.v5i1.326

Maloney, S., Nicklen, P., Rivers, G., Foo, J., Ooi, Y. Y., Reeves, S., et al. (2015). Cost-effectiveness analysis of blended versus face-to-face delivery of evidence-based medicine to medical students. J. Med. Internet Res. 17:e182. doi: 10.2196/jmir.4346

Mann, J. T., and Henneberry, S. R. (2014). Online versus face-to-face: students' preferences for college course attributes. J. Agric. Appl. Econ . 46, 1–19. doi: 10.1017/S1074070800000602

Mozes-Carmel, A., and Gold, S. S. (2009). A comparison of online vs proctored final exams in online classes. Imanagers J. Educ. Technol. 6, 76–81. doi: 10.26634/jet.6.1.212

Richardson, J. C., and Swan, K. (2003). Examining social presence in online courses in relation to student's perceived learning and satisfaction. J. Asynchr. Learn. 7, 68–88.

Roval, A. P., and Jordan, H. M. (2004). Blended learning and sense of community: a comparative analysis with traditional and fully online graduate courses. Int. Rev. Res. Open Dist. Learn. 5. doi: 10.19173/irrodl.v5i2.192

Salcedo, C. S. (2010). Comparative analysis of learning outcomes in face-to-face foreign language classes vs. language lab and online. J. Coll. Teach. Learn. 7, 43–54. doi: 10.19030/tlc.v7i2.88

Stern, B. S. (2004). A comparison of online and face-to-face instruction in an undergraduate foundations of american education course. Contemp. Issues Technol. Teach. Educ. J. 4, 196–213.

Summers, J. J., Waigandt, A., and Whittaker, T. A. (2005). A comparison of student achievement and satisfaction in an online versus a traditional face-to-face statistics class. Innov. High. Educ. 29, 233–250. doi: 10.1007/s10755-005-1938-x

Tanyel, F., and Griffin, J. (2014). A Ten-Year Comparison of Outcomes and Persistence Rates in Online versus Face-to-Face Courses . Retrieved from: https://www.westga.edu/~bquest/2014/onlinecourses2014.pdf

Werhner, M. J. (2010). A comparison of the performance of online versus traditional on-campus earth science students on identical exams. J. Geosci. Educ. 58, 310–312. doi: 10.5408/1.3559697

Westhuis, D., Ouellette, P. M., and Pfahler, C. L. (2006). A comparative analysis of on-line and classroom-based instructional formats for teaching social work research. Adv. Soc. Work 7, 74–88. doi: 10.18060/184

Wladis, C., Conway, K. M., and Hachey, A. C. (2015). The online STEM classroom-who succeeds? An exploration of the impact of ethnicity, gender, and non-traditional student characteristics in the community college context. Commun. Coll. Rev. 43, 142–164. doi: 10.1177/0091552115571729

Xu, D., and Jaggars, S. S. (2016). Performance gaps between online and face-to-face courses: differences across types of students and academic subject areas. J. Higher Educ. 85, 633–659. doi: 10.1353/jhe.2014.0028

Zhang, L.-C., and Worthington, A. C. (2017). Scale and scope economies of distance education in Australian universities. Stud. High. Educ. 42, 1785–1799. doi: 10.1080/03075079.2015.1126817

Keywords: face-to-face (F2F), traditional classroom teaching, web-based instructions, information and communication technology (ICT), online learning, desire to learn (D2L), passive learning, active learning

Citation: Paul J and Jefferson F (2019) A Comparative Analysis of Student Performance in an Online vs. Face-to-Face Environmental Science Course From 2009 to 2016. Front. Comput. Sci. 1:7. doi: 10.3389/fcomp.2019.00007

Received: 15 May 2019; Accepted: 15 October 2019; Published: 12 November 2019.

Reviewed by:

Copyright © 2019 Paul and Jefferson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jasmine Paul, paulj@fvsu.edu

• Open access
• Published: 16 September 2021

Online learning during COVID-19 produced equivalent or better student course performance as compared with pre-pandemic: empirical evidence from a school-wide comparative study

• Meixun Zheng 1 ,
• Daniel Bender 1 &
• Cindy Lyon 1  

BMC Medical Education volume  21 , Article number:  495 ( 2021 ) Cite this article

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The COVID-19 pandemic forced dental schools to close their campuses and move didactic instruction online. The abrupt transition to online learning, however, has raised several issues that have not been resolved. While several studies have investigated dental students’ attitude towards online learning during the pandemic, mixed results have been reported. Additionally, little research has been conducted to identify and understand factors, especially pedagogical factors, that impacted students’ acceptance of online learning during campus closure. Furthermore, how online learning during the pandemic impacted students’ learning performance has not been empirically investigated. In March 2020, the dental school studied here moved didactic instruction online in response to government issued stay-at-home orders. This first-of-its-kind comparative study examined students’ perceived effectiveness of online courses during summer quarter 2020, explored pedagogical factors impacting their acceptance of online courses, and empirically evaluated the impact of online learning on students’ course performance, during the pandemic.

The study employed a quasi-experimental design. Participants were 482 pre-doctoral students in a U.S dental school. Students’ perceived effectiveness of online courses during the pandemic was assessed with a survey. Students’ course grades for online courses during summer quarter 2020 were compared with that of a control group who received face-to-face instruction for the same courses before the pandemic in summer quarter 2019.

Survey results revealed that most online courses were well accepted by the students, and 80 % of them wanted to continue with some online instruction post pandemic. Regression analyses revealed that students’ perceived engagement with faculty and classmates predicted their perceived effectiveness of the online course. More notably, Chi Square tests demonstrated that in 16 out of the 17 courses compared, the online cohort during summer quarter 2020 was equally or more likely to get an A course grade than the analogous face-to-face cohort during summer quarter 2019.

Conclusions

This is the first empirical study in dental education to demonstrate that online courses during the pandemic could achieve equivalent or better student course performance than the same pre-pandemic in-person courses. The findings fill in gaps in literature and may inform online learning design moving forward.

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Introduction

Research across disciplines has demonstrated that well-designed online learning can lead to students’ enhanced motivation, satisfaction, and learning [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. A report by the U.S. Department of Education [ 8 ], based on examinations of comparative studies of online and face-to-face versions of the same course from 1996 to 2008, concluded that online learning could produce learning outcomes equivalent to or better than face-to-face learning. The more recent systematic review by Pei and Wu [ 9 ] provided additional evidence that online learning is at least as effective as face-to-face learning for undergraduate medical students.

To take advantage of the opportunities presented by online learning, thought leaders in dental education in the U.S. have advocated for the adoption of online learning in the nation’s dental schools [ 10 , 11 , 12 ]. However, digital innovation has been a slow process in academic dentistry [ 13 , 14 , 15 ]. In March 2020, the COVID-19 pandemic brought unprecedented disruption to dental education by necessitating the need for online learning. In accordance with stay-at-home orders to prevent the spread of the virus, dental schools around the world closed their campuses and moved didactic instruction online.

The abrupt transition to online learning, however, has raised several concerns and question. First, while several studies have examined dental students’ online learning satisfaction during the pandemic, mixed results have been reported. Some studies have reported students’ positive attitude towards online learning [ 15 , 16 , 17 , 18 , 19 , 20 ]. Sadid-Zadeh et al. [ 18 ] found that 99 % of the surveyed dental students at University of Buffalo, in the U.S., were satisfied with live web-based lectures during the pandemic. Schlenz et al. [ 15 ] reported that students in a German dental school had a favorable attitude towards online learning and wanted to continue with online instruction in their future curriculum. Other studies, however, have reported students’ negative online learning experience during the pandemic [ 21 , 22 , 23 , 24 , 25 , 26 ]. For instance, dental students at Harvard University felt that learning during the pandemic had worsened and engagement had decreased [ 23 , 24 ]. In a study with medical and dental students in Pakistan, Abbasi et al. [ 21 ] found that 77 % of the students had negative perceptions about online learning and 84 % reported reduced student-instructor interactions.

In addition to these mixed results, little attention has been given to factors affecting students’ acceptance of online learning during the pandemic. With the likelihood that online learning will persist post pandemic [ 27 ], research in this area is warranted to inform online course design moving forward. In particular, prior research has demonstrated that one of the most important factors influencing students’ performance in any learning environment is a sense of belonging, the feeling of being connected with and supported by the instructor and classmates [ 28 , 29 , 30 , 31 ]. Unfortunately, this aspect of the classroom experience has suffered during school closure. While educational events can be held using a video conferencing system, virtual peer interaction on such platforms has been perceived by medical trainees to be not as easy and personal as physical interaction [ 32 ]. The pandemic highlights the need to examine instructional strategies most suited to the current situation to support students’ engagement with faculty and classmates.

Furthermore, there is considerable concern from the academic community about the quality of online learning. Pre-pandemic, some faculty and students were already skeptical about the value of online learning [ 33 ]. The longer the pandemic lasts, the more they may question the value of online education, asking: Can online learning during the pandemic produce learning outcomes that are similar to face-to-face learning before the pandemic? Despite the documented benefits of online learning prior to the pandemic, the actual impact of online learning during the pandemic on students’ academic performance is still unknown due to reasons outlined below.

On one hand, several factors beyond the technology used could influence the effectiveness of online learning, one of which is the teaching context [ 34 ]. The sudden transition to online learning has posed many challenges to faculty and students. Faculty may not have had adequate time to carefully design online courses to take full advantage of the possibilities of the online format. Some faculty may not have had prior online teaching experience and experienced a deeper learning curve when it came to adopting online teaching methods [ 35 ]. Students may have been at the risk of increased anxiety due to concerns about contracting the virus, on time graduation, finances, and employment [ 36 , 37 ], which may have negatively impacted learning performance [ 38 ]. Therefore, whether online learning during the pandemic could produce learning outcomes similar to those of online learning implemented during more normal times remains to be determined.

Most existing studies on online learning in dental education during the pandemic have only reported students’ satisfaction. The actual impact of the online format on academic performance has not been empirically investigated. The few studies that have examined students’ learning outcomes have only used students’ self-reported data from surveys and focus groups. According to Kaczmarek et al. [ 24 ], 50 % of the participating dental faculty at Harvard University perceived student learning to have worsened during the pandemic and 70 % of the students felt the same. Abbasi et al. [ 21 ] reported that 86 % of medical and dental students in a Pakistan college felt that they learned less online. While student opinions are important, research has demonstrated a poor correlation between students’ perceived learning and actual learning gains [ 39 ]. As we continue to navigate the “new normal” in teaching, students’ learning performance needs to be empirically evaluated to help institutions gauge the impact of this grand online learning experiment.

Research purposes

In March 2020, the University of the Pacific Arthur A. Dugoni School of Dentistry, in the U.S., moved didactic instruction online to ensure the continuity of education during building closure. This study examined students’ acceptance of online learning during the pandemic and its impacting factors, focusing on instructional practices pertaining to students’ engagement/interaction with faculty and classmates. Another purpose of this study was to empirically evaluate the impact of online learning during the pandemic on students’ actual course performance by comparing it with that of a pre-pandemic cohort. To understand the broader impact of the institutional-wide online learning effort, we examined all online courses offered in summer quarter 2020 (July to September) that had a didactic component.

This is the first empirical study in dental education to evaluate students’ learning performance during the pandemic. The study aimed to answer the following three questions.

How well was online learning accepted by students, during the summer quarter 2020 pandemic interruption?

How did instructional strategies, centered around students’ engagement with faculty and classmates, impact their acceptance of online learning?

How did online learning during summer quarter 2020 impact students’ course performance as compared with a previous analogous cohort who received face-to-face instruction in summer quarter 2019?

This study employed a quasi-experimental design. The study was approved by the university’s institutional review board (#2020-68).

Study context and participants

The study was conducted at the Arthur A. Dugoni School of Dentistry, University of the Pacific. The program runs on a quarter system. It offers a 3-year accelerated Doctor of Dental Surgery (DDS) program and a 2-year International Dental Studies (IDS) program for international dentists who have obtained a doctoral degree in dentistry from a country outside the U.S. and want to practice in the U.S. Students advance throughout the program in cohorts. IDS students take some courses together with their DDS peers. All three DDS classes (D1/DDS 2023, D2/DDS 2022, and D3/DDS 2021) and both IDS classes (I1/IDS 2022 and I2/IDS 2021) were invited to participate in the study. The number of students in each class was: D1 = 145, D2 = 143, D3 = 143, I1 = 26, and I2 = 25. This resulted in a total of 482 student participants.

During campus closure, faculty delivered remote instruction in various ways, including live online classes via Zoom @  [ 40 ], self-paced online modules on the school’s learning management system Canvas @  [ 41 ], or a combination of live and self-paced delivery. For self-paced modules, students studied assigned readings and/or viewings such as videos and pre-recorded slide presentations. Some faculty also developed self-paced online lessons with SoftChalk @  [ 42 ], a cloud-based platform that supports the inclusion of gamified learning by insertion of various mini learning activities. The SoftChalk lessons were integrated with Canvas @  [ 41 ] and faculty could monitor students’ progress. After students completed the pre-assigned online materials, some faculty held virtual office hours or live online discussion sessions for students to ask questions and discuss key concepts.

Data collection and analysis

Student survey.

Students’ perceived effectiveness of summer quarter 2020 online courses was evaluated by the school’s Office of Academic Affairs in lieu of the regular course evaluation process. A total of 19 courses for DDS students and 10 courses for IDS students were evaluated. An 8-question survey developed by the researchers (Additional file 1 ) was administered online in the last week of summer quarter 2020. Course directors invited student to take the survey during live online classes. The survey introduction stated that taking the survey was voluntary and that their anonymous responses would be reported in aggregated form for research purposes. Students were invited to continue with the survey if they chose to participate; otherwise, they could exit the survey. The number of students in each class who took the survey was as follows: D1 ( n  = 142; 98 %), D2 ( n  = 133; 93 %), D3 ( n  = 61; 43 %), I1 ( n  = 23; 88 %), and I2 ( n  = 20; 80 %). This resulted in a total of 379 (79 %) respondents across all classes.

The survey questions were on a 4-point scale, ranging from Strongly Disagree (1 point), Disagree (2 points), Agree (3 points), and Strongly Agree (4 points). Students were asked to rate each online course by responding to four statements: “ I could fully engage with the instructor and classmates in this course”; “The online format of this course supported my learning”; “Overall this online course is effective.”, and “ I would have preferred face-to-face instruction for this course ”. For the first three survey questions, a higher mean score indicated a more positive attitude toward the online course. For the fourth question “ I would have preferred face-to-face instruction for this course ”, a higher mean score indicated that more students would have preferred face-to-face instruction for the course. Two additional survey questions asked students to select their preferred online delivery method for fully online courses during the pandemic from three given choices (synchronous online/live, asynchronous online/self-paced, and a combination of both), and to report whether they wanted to continue with some online instruction post pandemic. Finally, two open-ended questions at the end of the survey allowed students to comment on the aspects of online format that they found to be helpful and to provide suggestion for improvement. For the purpose of this study, we focused on the quantitative data from the Likert-scale questions.

Descriptive data such as the mean scores were reported for each course. Regression analyses were conducted to examine the relationship between instructional strategies focusing on students’ engagement with faculty and classmates, and their overall perceived effectiveness of the online course. The independent variable was student responses to the question “ I could fully engage with the instructor and classmates in this course ”, and the dependent variable was their answer to the question “ Overall, this online course is effective .”

Student course grades

Using Chi-square tests, student course grade distributions (A, B, C, D, and F) for summer quarter 2020 online courses were compared with that of a previous cohort who received face-to-face instruction for the same course in summer quarter 2019. Note that as a result of the school’s pre-doctoral curriculum redesign implemented in July 2019, not all courses offered in summer quarter 2020 were offered in the previous year in summer quarter 2019. In other words, some of the courses offered in summer quarter 2020 were new courses offered for the first time. Because these new courses did not have a previous face-to-face version to compare to, they were excluded from data analysis. For some other courses, while course content remained the same between 2019 and 2020, the sequence of course topics within the course had changed. These courses were also excluded from data analysis.

After excluding the aforementioned courses, it resulted in a total of 17 “comparable” courses that were included in data analysis (see the subsequent section). For these courses, the instructor, course content, and course goals were the same in both 2019 and 2020. The assessment methods and grading policies also remained the same through both years. For exams and quizzes, multiple choice questions were the dominating format for both years. While some exam questions in 2020 were different from 2019, faculty reported that the overall exam difficulty level was similar. The main difference in assessment was testing conditions. The 2019 cohort took computer-based exams in the physical classroom with faculty proctoring, and the 2020 cohort took exams at home with remote proctoring to ensure exam integrity. The remote proctoring software monitored the student during the exam through a web camera on their computer/laptop. The recorded video file flags suspicious activities for faculty review after exam completion.

Students’ perceived effectiveness of online learning

Table  1 summarized data on DDS students’ perceived effectiveness of each online course during summer quarter 2020. For the survey question “ Overall, this online course is effective ”, the majority of courses received a mean score that was approaching or over 3 points on the 4-point scale, suggesting that online learning was generally well accepted by students. Despite overall positive online course experiences, for many of the courses examined, there was an equal split in student responses to the question “ I would have preferred face-to-face instruction for this course .” Additionally, for students’ preferred online delivery method for fully online courses, about half of the students in each class preferred a combination of synchronous and asynchronous online learning (see Fig.  1 ). Finally, the majority of students wanted faculty to continue with some online instruction post pandemic: D1class (110; 78.60 %), D2 class (104; 80 %), and D3 class (49; 83.10 %).

While most online courses received favorable ratings, some variations did exist among courses. For D1 courses, “ Anatomy & Histology ” received lower ratings than others. This could be explained by its lab component, which didn’t lend itself as well to the online format. For D2 courses, several of them received lower ratings than others, especially for the survey question on students’ perceived engagement with faculty and classmates.

DDS students’ preferred online delivery method for fully online courses

Table  2 summarized IDS students’ perceived effectiveness of each online course during summer quarter 2020. For the survey question “ Overall, this online course is effective ”, all courses received a mean score that was approaching or over 3 points on a 4-point scale, suggesting that online learning was well accepted by students. For the survey question “ I would have preferred face-to-face instruction for this course ”, for most online courses examined, the percentage of students who would have preferred face-to-face instruction was similar to that of students who preferred online instruction for the course. Like their DDS peers, about half of the IDS students in each class also preferred a combination of synchronous and asynchronous online delivery for fully online courses (See Fig.  2 ). Finally, the majority of IDS students (I1, n = 18, 81.80 %; I2, n = 16, 84.20 %) wanted to continue with some online learning after the pandemic is over.

IDS students’ preferred online delivery method for fully online courses

Factors impacting students’ acceptance of online learning

For all 19 online courses taken by DDS students, regression analyses indicated that there was a significantly positive relationship between students’ perceived engagement with faculty and classmates and their perceived effectiveness of the course. P value was 0.00 across all courses. The ranges of effect size (r 2 ) were: D1 courses (0.26 to 0.50), D2 courses (0.39 to 0.650), and D3 courses (0.22 to 0.44), indicating moderate to high correlations across courses.

For 9 out of the 10 online courses taken by IDS students, there was a positive relationship between students’ perceived engagement with faculty and classmates and their perceived effectiveness of the course. P value was 0.00 across courses. The ranges of effect size were: I1 courses (0.35 to 0.77) and I2 courses (0.47 to 0.63), indicating consistently high correlations across courses. The only course in which students’ perceived engagement with faculty and classmates didn’t predict perceived effective of the course was “ Integrated Clinical Science III (ICS III) ”, which the I2 class took together with their D3 peers.

Impact of online learning on students’ course performance

Chi square test results (Table  3 ) indicated that in 4 out of the 17 courses compared, the online cohort during summer quarter 2020 was more likely to receive an A grade than the face-to-face cohort during summer quarter 2019. In 12 of the courses, the online cohort were equally likely to receive an A grade as the face-to-face cohort. In the remaining one course, the online cohort was less likely to receive an A grade than the face-to-face cohort.

Students’ acceptance of online learning during the pandemic

Survey results revealed that students had generally positive perceptions about online learning during the pandemic and the majority of them wanted to continue with some online learning post pandemic. Overall, our findings supported several other studies in dental [ 18 , 20 ], medical [ 43 , 44 ], and nursing [ 45 ] education that have also reported students’ positive attitudes towards online learning during the pandemic. In their written comments in the survey, students cited enhanced flexibility as one of the greatest benefits of online learning. Some students also commented that typing questions in the chat box during live online classes was less intimidating than speaking in class. Others explicitly stated that not having to commute to/from school provided more time for sleep, which helped with self-care and mental health. Our findings are in line with previous studies which have also demonstrated that online learning offered higher flexibility [ 46 , 47 ]. Meanwhile, consistent with findings of other researchers [ 19 , 21 , 46 ], our students felt difficulty engaging with faculty and classmates in several online courses.

There were some variations among individual courses in students’ acceptance of the online format. One factor that could partially account for the observed differences was instructional strategies. In particular, our regression analysis results demonstrated a positive correlation between students’ perceived engagement with faculty and classmates and their perceived overall effectiveness of the online course. Other aspects of course design might also have influenced students’ overall rating of the online course. For instance, some D2 students commented that the requirements of the course “ Integrated Case-based Seminars (ICS II) ” were not clear and that assessment did not align with lecture materials. It is important to remember that communicating course requirements clearly and aligning course content and assessment are principles that should be applied in any course, whether face-to-face or online. Our results highlighted the importance of providing faculty training on basic educational design principles and online learning design strategies. Furthermore, the nature of the course might also have impacted student ratings. For example, D1 course “ Anatomy and Histology ” had a lab component, which did not lend itself as well to the online format. Many students reported that it was difficult to see faculty’s live demonstration during Zoom lectures, which may have resulted in a lower student satisfaction rating.

As for students’ preferred online delivery method for fully online courses during the pandemic, about half of them preferred a combination of synchronous and asynchronous online learning. In light of this finding, as we continue with remote learning until public health directives allow a return to campus, we will encourage faculty to integrate these two online delivery modalities. Finally, in view of the result that over 80 % of the students wanted to continue with some online instruction after the pandemic, the school will advocate for blended learning in the post-pandemic world [ 48 ]. For future face-to-face courses on campus after the pandemic, faculty are encouraged to deliver some content online to reduce classroom seat time and make learning more flexible. Taken together, our findings not only add to the overall picture of the current situation but may inform learning design moving forward.

Role of online engagement and interaction

To reiterate, we found that students’ perceived engagement with faculty and classmates predicted their perceived overall effectiveness of the online course. This aligns with the larger literature on best practices in online learning design. Extensive research prior to the pandemic has confirmed that the effectiveness of online learning is determined by a number of factors beyond the tools used, including students’ interactions with the instructor and classmates [ 49 , 50 , 51 , 52 ]. Online students may feel isolated due to reduced or lack of interaction [ 53 , 54 ]. Therefore, in designing online learning experiences, it is important to remember that learning is a social process [ 55 ]. Faculty’s role is not only to transmit content but also to promote the different types of interactions that are an integral part of the online learning process [ 33 ]. The online teaching model in which faculty uploads materials online but teach it in the same way as in the physical classroom, without special effort to engage students, doesn’t make the best use of the online format. Putting the “sage on the screen” during a live class meeting on a video conferencing system is not different from “sage on the stage” in the physical classroom - both provide limited space for engagement. Such one-way monologue devalues the potentials that online learning presents.

In light of the critical role that social interaction plays in online learning, faculty are encouraged to use the interactive features of online learning platforms to provide clear channels for student-instructor and student-student interactions. In the open-ended comments, students highlighted several instructional strategies that they perceived to be helpful for learning. For live online classes, these included conducting breakout room activities, using the chat box to facilitate discussions, polling, and integrating gameplay with apps such as Kahoot! @  [ 56 ]. For self-paced classes, students appreciated that faculty held virtual office hours or subsequent live online discussion sessions to reinforce understanding of the pre-assigned materials.

Quality of online education during the pandemic

This study provided empirical evidence in dental education that it was possible to ensure the continuity of education without sacrificing the quality of education provided to students during forced migration to distance learning upon building closure. To reiterate, in all but one online course offered in summer quarter 2020, students were equally or more likely to get an A grade than the face-to-face cohort from summer quarter 2019. Even for courses that had less student support for the online format (e.g., the D1 course “ Anatomy and Histology ”), there was a significant increase in the number of students who earned an A grade in 2020 as compared with the previous year. The reduced capacity for technical training during the pandemic may have resulted in more study time for didactic content. Overall, our results resonate with several studies in health sciences education before the pandemic that the quality of learning is comparable in face-to-face and online formats [ 9 , 57 , 58 ]. For the only course ( Integrated Case-based Seminars ICS II) in which the online cohort had inferior performance than the face-to-face cohort, as mentioned earlier, students reported that assessment was not aligned with course materials and that course expectations were not clear. This might explain why students’ course performance was not as strong as expected.

Limitations

This study used a pre-existing control group from the previous year. There may have been individual differences between students in the online and the face-to-face cohorts, such as motivation, learning style, and prior knowledge, that could have impacted the observed outcomes. Additionally, even though course content and assessment methods were largely the same in 2019 and 2020, changes in other aspects of the course could have impacted students’ course performance. Some faculty may have been more compassionate with grading (e.g., more flexible with assignment deadlines) in summer quarter 2020 given the hardship students experienced during the pandemic. On the other hand, remote proctoring in summer quarter 2020 may have heightened some students’ exam anxiety knowing that they were being monitored through a webcam. The existence and magnitude of effect of these factors needs to be further investigated.

This present study only examined the correlation between students’ perceived online engagement and their perceived overall effectiveness of the online course. Other factors that might impact their acceptance of the online format need to be further researched in future studies. Another future direction is to examine how students’ perceived online engagement correlates with their actual course performance. Because the survey data collected for our present study are anonymous, we cannot match students’ perceived online engagement data with their course grades to run this additional analysis. It should also be noted that this study was focused on didactic online instruction. Future studies might examine how technical training was impacted during the COVID building closure. It was also out of the scope of this study to examine how student characteristics, especially high and low academic performance as reflected by individual grades, affects their online learning experience and performance. We plan to conduct a follow-up study to examine which group of students are most impacted by the online format. Finally, this study was conducted in a single dental school, and so the findings may not be generalizable to other schools and disciplines. Future studies could be conducted in another school or disciplines to compare results.

This study revealed that dental students had generally favorable attitudes towards online learning during the COVID-19 pandemic and that their perceived engagement with faculty and classmates predicted their acceptance of the online course. Most notably, this is the first study in dental education to demonstrate that online learning during the pandemic could achieve similar or better learning outcomes than face-to-face learning before the pandemic. Findings of our study could contribute significantly to the literature on online learning during the COVID-19 pandemic in health sciences education. The results could also inform future online learning design as we re-envision the future of online learning.

Availability of data and materials

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

Bello G, Pennisi MA, Maviglia R, Maggiore SM, Bocci MG, Montini L, et al. Online vs live methods for teaching difficult airway management to anesthesiology residents. Intensive Care Med. 2005; 31 (4): 547–552.

Article   Google Scholar  

Ruiz JG, Mintzer MJ, Leipzig RM. The impact of e-learning in medical education. Acad Med. 2006; 81(3): 207–12.

Kavadella A, Tsiklakis K, Vougiouklakis G, Lionarakis A. Evaluation of a blended learning course for teaching oral radiology to undergraduate dental students. Eur J Dent Educ. 2012; 16(1): 88–95.

de Jong N, Verstegen DL, Tan FS, O’Connor SJ. A comparison of classroom and online asynchronous problem-based learning for students undertaking statistics training as part of a public health master’s degree. Adv Health Sci Educ. 2013; 18(2):245–64.

Hegeman JS. Using instructor-generated video lectures in online mathematics coursesimproves student learning. Online Learn. 2015;19(3):70–87.

Gaupp R, Körner M, Fabry G. Effects of a case-based interactive e-learning course on knowledge and attitudes about patient safety: a quasi-experimental study with third-year medical students. BMC Med Educ. 2016; 16(1):172.

Zheng M, Bender D, Reid L, Milani J. An interactive online approach to teaching evidence-based dentistry with Web 2.0 technology. J Dent Educ. 2017; 81(8): 995–1003.

Means B, Toyama Y, Murphy R, Bakia M, Jones K. Evaluation of evidence-based practices in online learning: A meta-analysis and review of online learning studies. U.S. Department of Education, Office of Planning, Evaluation and Policy Development. Washington D.C. 2009.

Google Scholar  

Pei L, Wu H. Does online learning work better than offline learning in undergraduate medical education? A systematic review and meta-analysis. Med Educ Online. 2019; 24(1):1666538.

Andrews KG, Demps EL. Distance education in the U.S. and Canadian undergraduate dental curriculum. J Dent Educ. 2003; 67(4):427–38.

Kassebaum DK, Hendricson WD, Taft T, Haden NK. The dental curriculum at North American dental institutions in 2002–03: a survey of current structure, recent innovations, and planned changes. J Dent Educ. 2004; 68(9):914–931.

Haden NK, Hendricson WD, Kassebaum DK, Ranney RR, Weinstein G, Anderson EL, et al. Curriculum changes in dental education, 2003–09. J Dent Educ. 2010; 74(5):539–57.

DeBate RD, Cragun D, Severson HH, Shaw T, Christiansen S, Koerber A, et al. Factors for increasing adoption of e-courses among dental and dental hygiene faculty members. J Dent Educ. 2011; 75 (5): 589–597.

Saeed SG, Bain J, Khoo E, Siqueira WL. COVID-19: Finding silver linings for dental education. J Dent Educ. 2020; 84(10):1060–1063.

Schlenz MA, Schmidt A, Wöstmann B, Krämer N, Schulz-Weidner N. Students’ and lecturers’ perspective on the implementation of online learning in dental education due to SARS-CoV-2 (COVID-19): a cross-sectional study. BMC Med Educ. 2020;20(1):1–7.

Donn J, Scott JA, Binnie V, Bell A. A pilot of a virtual Objective Structured Clinical Examination in dental education. A response to COVID-19. Eur J Dent Educ. 2020; https://doi.org/10.1111/eje.12624

Hung M, Licari FW, Hon ES, Lauren E, Su S, Birmingham WC, Wadsworth LL, Lassetter JH, Graff TC, Harman W, et al. In an era of uncertainty: impact of COVID-19 on dental education. J Dent Educ. 2020; 85 (2): 148–156.

Sadid-Zadeh R, Wee A, Li R, Somogyi‐Ganss E. Audience and presenter comparison of live web‐based lectures and traditional classroom lectures during the COVID‐19 pandemic. J Prosthodont. 2020. doi: https://doi.org/10.1111/jopr.13301

Wang K, Zhang L, Ye L. A nationwide survey of online teaching strategies in dental education in China. J Dent Educ. 2020; 85 (2): 128–134.

Rad FA, Otaki F, Baqain Z, Zary N, Al-Halabi M. Rapid transition to distance learning due to COVID-19: Perceptions of postgraduate dental learners and instructors. PLoS One. 2021; 16(2): e0246584.

Abbasi S, Ayoob T, Malik A, Memon SI. Perceptions of students regarding E-learning during Covid-19 at a private medical college. Pak J Med Sci. 2020; 3 6 : 57–61.

Al-Azzam N, Elsalem L, Gombedza F. A cross-sectional study to determine factors affecting dental and medical students’ preference for virtual learning during the COVID-19 outbreak. Heliyon. 6(12). 2020. doi: https://doi.org/10.1016/j.heliyon.2020.e05704

Chen E, Kaczmarek K, Ohyama H. Student perceptions of distance learning strategies during COVID-19. J Dent Educ. 2020. doi: https://doi.org/10.1002/jdd.12339

Kaczmarek K, Chen E, Ohyama H. Distance learning in the COVID-19 era: Comparison of student and faculty perceptions. J Dent Educ. 2020. https://doi.org/10.1002/jdd.12469

Sarwar H, Akhtar H, Naeem MM, Khan JA, Waraich K, Shabbir S, et al. Self-reported effectiveness of e-learning classes during COVID-19 pandemic: A nation-wide survey of Pakistani undergraduate dentistry students. Eur J Dent. 2020; 14 (S01): S34-S43.

Al-Taweel FB, Abdulkareem AA, Gul SS, Alshami ML. Evaluation of technology‐based learning by dental students during the pandemic outbreak of coronavirus disease 2019. Eur J Dent Educ. 2021; 25(1): 183–190.

Elangovan S, Mahrous A, Marchini L. Disruptions during a pandemic: Gaps identified and lessons learned. J Dent Educ. 2020; 84 (11): 1270–1274.

Goodenow C. Classroom belonging among early adolescent students: Relationships to motivation and achievement. J Early Adolesc.1993; 13(1): 21–43.

Goodenow C. The psychological sense of school membership among adolescents: Scale development and educational correlates. Psychol Sch. 1993; 30(1): 79–90.

St-Amand J, Girard S, Smith J. Sense of belonging at school: Defining attributes, determinants, and sustaining strategies. IAFOR Journal of Education. 2017; 5(2):105–19.

Peacock S, Cowan J. Promoting sense of belonging in online learning communities of inquiry at accredited courses. Online Learn. 2019; 23(2): 67–81.

Chan GM, Kanneganti A, Yasin N, Ismail-Pratt I, Logan SJ. Well‐being, obstetrics and gynecology and COVID‐19: Leaving no trainee behind. Aust N Z J Obstet Gynaecol. 2020; 60(6): 983–986.

Hodges C, Moore S, Lockee B, Trust T, Bond A. The difference between emergency remote teaching and online learning. Educause Review. 2020; 2 7 , 1–12.

Means B, Bakia M, Murphy R. Learning online: What research tells us about whether, when and how. Routledge. 2014.

Iyer P, Aziz K, Ojcius DM. Impact of COVID-19 on dental education in the United States. J Dent Educ. 2020; 84(6): 718–722.

Machado RA, Bonan PRF, Perez DEDC, Martelli JÚnior H. 2020. COVID-19 pandemic and the impact on dental education: Discussing current and future perspectives. Braz Oral Res. 2020; 34: e083.

Wu DT, Wu KY, Nguyen TT, Tran SD. The impact of COVID-19 on dental education in North America-Where do we go next? Eur J Dent Educ. 2020; 24(4): 825–827.

de Oliveira Araújo FJ, de Lima LSA, Cidade PIM, Nobre CB, Neto MLR. Impact of Sars-Cov-2 and its reverberation in global higher education and mental health. Psychiatry Res. 2020; 288:112977. doi: https://doi.org/10.1016/j.psychres.2020.112977

Persky AM, Lee E, Schlesselman LS. Perception of learning versus performance as outcome measures of educational research. Am J Pharm Educ. 2020; 8 4 (7): ajpe7782.

Zoom @ . Zoom Video Communications , San Jose, CA, USA. https://zoom.us/

Canvas @ . Instructure, INC. Salt Lake City, UT, USA. https://www.instructure.com/canvas

SoftChalk @ . SoftChalk LLC . San Antonio, TX, USA. https://www.softchalkcloud.com/

Agarwal S, Kaushik JS. Student’s perception of online learning during COVID pandemic. Indian J Pediatr. 2020; 87: 554–554.

Khalil R, Mansour AE, Fadda WA, Almisnid K, Aldamegh M, Al-Nafeesah A, et al. The sudden transition to synchronized online learning during the COVID-19 pandemic in Saudi Arabia: a qualitative study exploring medical students’ perspectives. BMC Med Educ. 2020; 20(1): 1–10.

Riley E, Capps N, Ward N, McCormack L, Staley J. Maintaining academic performance and student satisfaction during the remote transition of a nursing obstetrics course to online instruction. Online Learn. 2021; 25(1), 220–229.

Amir LR, Tanti I, Maharani DA, Wimardhani YS, Julia V, Sulijaya B, et al. Student perspective of classroom and distance learning during COVID-19 pandemic in the undergraduate dental study program Universitas Indonesia. BMC Med Educ. 2020; 20(1):1–8.

Dost S, Hossain A, Shehab M, Abdelwahed A, Al-Nusair L. Perceptions of medical students towards online teaching during the COVID-19 pandemic: a national cross-sectional survey of 2721 UK medical students. BMJ Open. 2020; 10(11).

Graham CR, Woodfield W, Harrison JB. A framework for institutional adoption and implementation of blended learning in higher education. Internet High Educ. 2013; 18 : 4–14.

Sing C, Khine M. An analysis of interaction and participation patterns in online community. J Educ Techno Soc. 2006; 9(1): 250–261.

Bernard RM, Abrami PC, Borokhovski E, Wade CA, Tamim RM, Surkes MA, et al. A meta-analysis of three types of interaction treatments in distance education. Rev Educ Res. 2009; 79(3): 1243–1289.

Fedynich L, Bradley KS, Bradley J. Graduate students’ perceptions of online learning. Res High Educ. 2015; 27.

Tanis CJ. The seven principles of online learning: Feedback from faculty and alumni on its importance for teaching and learning. Res Learn Technol. 2020; 28 . https://doi.org/10.25304/rlt.v28.2319

Dixson MD. Measuring student engagement in the online course: The Online Student Engagement scale (OSE). Online Learn. 2015; 19 (4).

Kwary DA, Fauzie S. Students’ achievement and opinions on the implementation of e-learning for phonetics and phonology lectures at Airlangga University. Educ Pesqui. 2018; 44 .

Vygotsky LS. Mind in society: The development of higher psychological processes. Cambridge (MA): Harvard University Press. 1978.

Kahoot! @ . Oslo, Norway. https://kahoot.com/

Davis J, Chryssafidou E, Zamora J, Davies D, Khan K, Coomarasamy A. Computer-based teaching is as good as face to face lecture-based teaching of evidence-based medicine: a randomised controlled trial. BMC Med Educ. 2007; 7(1): 1–6.

Davis J, Crabb S, Rogers E, Zamora J, Khan K. Computer-based teaching is as good as face to face lecture-based teaching of evidence-based medicine: a randomized controlled trial. Med Teach. 2008; 30(3): 302–307.

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MZ is an Associate Professor of Learning Sciences and Senior Instructional Designer at School of Dentistry, University of the Pacific. She has a PhD in Education, with a specialty on learning sciences and technology. She has dedicated her entire career to conducting research on online learning, learning technology, and faculty development. Her research has resulted in several peer-reviewed publications in medical, dental, and educational technology journals. MZ has also presented regularly at national conferences.

DB is an Assistant Dean for Academic Affairs at School of Dentistry, University of the Pacific. He has an EdD degree in education, with a concentration on learning and instruction. Over the past decades, DB has been overseeing and delivering faculty pedagogical development programs to dental faculty. His research interest lies in educational leadership and instructional innovation. DB has co-authored several peer-reviewed publications in health sciences education and presented regularly at national conferences.

CL is Associate Dean of Oral Healthcare Education, School of Dentistry, University of the Pacific. She has a Doctor of Dental Surgery (DDS) degree and an EdD degree with a focus on educational leadership. Her professional interest lies in educational leadership, oral healthcare education innovation, and faculty development. CL has co-authored several publications in peer-reviewed journals in health sciences education and presented regularly at national conferences.

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Zheng, M., Bender, D. & Lyon, C. Online learning during COVID-19 produced equivalent or better student course performance as compared with pre-pandemic: empirical evidence from a school-wide comparative study. BMC Med Educ 21 , 495 (2021). https://doi.org/10.1186/s12909-021-02909-z

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Students’ experience of online learning during the COVID‐19 pandemic: A province‐wide survey study

Lixiang yan.

1 Centre for Learning Analytics at Monash, Faculty of Information Technology, Monash University, Clayton VIC, Australia

Alexander Whitelock‐Wainwright

2 Portfolio of the Deputy Vice‐Chancellor (Education), Monash University, Melbourne VIC, Australia

Quanlong Guan

3 Department of Computer Science, Jinan University, Guangzhou China

Gangxin Wen

4 College of Cyber Security, Jinan University, Guangzhou China

Dragan Gašević

Guanliang chen, associated data.

The data is not openly available as it is restricted by the Chinese government.

Online learning is currently adopted by educational institutions worldwide to provide students with ongoing education during the COVID‐19 pandemic. Even though online learning research has been advancing in uncovering student experiences in various settings (i.e., tertiary, adult, and professional education), very little progress has been achieved in understanding the experience of the K‐12 student population, especially when narrowed down to different school‐year segments (i.e., primary and secondary school students). This study explores how students at different stages of their K‐12 education reacted to the mandatory full‐time online learning during the COVID‐19 pandemic. For this purpose, we conducted a province‐wide survey study in which the online learning experience of 1,170,769 Chinese students was collected from the Guangdong Province of China. We performed cross‐tabulation and Chi‐square analysis to compare students’ online learning conditions, experiences, and expectations. Results from this survey study provide evidence that students’ online learning experiences are significantly different across school years. Foremost, policy implications were made to advise government authorises and schools on improving the delivery of online learning, and potential directions were identified for future research into K‐12 online learning.

Practitioner notes

What is already known about this topic

• Online learning has been widely adopted during the COVID‐19 pandemic to ensure the continuation of K‐12 education.
• Student success in K‐12 online education is substantially lower than in conventional schools.
• Students experienced various difficulties related to the delivery of online learning.

What this paper adds

• Provide empirical evidence for the online learning experience of students in different school years.
• Identify the different needs of students in primary, middle, and high school.
• Identify the challenges of delivering online learning to students of different age.

Implications for practice and/or policy

• Authority and schools need to provide sufficient technical support to students in online learning.
• The delivery of online learning needs to be customised for students in different school years.

INTRODUCTION

The ongoing COVID‐19 pandemic poses significant challenges to the global education system. By July 2020, the UN Educational, Scientific and Cultural Organization (2020) reported nationwide school closure in 111 countries, affecting over 1.07 billion students, which is around 61% of the global student population. Traditional brick‐and‐mortar schools are forced to transform into full‐time virtual schools to provide students with ongoing education (Van Lancker & Parolin,  2020 ). Consequently, students must adapt to the transition from face‐to‐face learning to fully remote online learning, where synchronous video conferences, social media, and asynchronous discussion forums become their primary venues for knowledge construction and peer communication.

For K‐12 students, this sudden transition is problematic as they often lack prior online learning experience (Barbour & Reeves,  2009 ). Barbour and LaBonte ( 2017 ) estimated that even in countries where online learning is growing rapidly, such as USA and Canada, less than 10% of the K‐12 student population had prior experience with this format. Maladaptation to online learning could expose inexperienced students to various vulnerabilities, including decrements in academic performance (Molnar et al.,  2019 ), feeling of isolation (Song et al.,  2004 ), and lack of learning motivation (Muilenburg & Berge,  2005 ). Unfortunately, with confirmed cases continuing to rise each day, and new outbreaks occur on a global scale, full‐time online learning for most students could last longer than anticipated (World Health Organization,  2020 ). Even after the pandemic, the current mass adoption of online learning could have lasting impacts on the global education system, and potentially accelerate and expand the rapid growth of virtual schools on a global scale (Molnar et al.,  2019 ). Thus, understanding students' learning conditions and their experiences of online learning during the COVID pandemic becomes imperative.

Emerging evidence on students’ online learning experience during the COVID‐19 pandemic has identified several major concerns, including issues with internet connection (Agung et al.,  2020 ; Basuony et al.,  2020 ), problems with IT equipment (Bączek et al.,  2021 ; Niemi & Kousa,  2020 ), limited collaborative learning opportunities (Bączek et al.,  2021 ; Yates et al.,  2020 ), reduced learning motivation (Basuony et al.,  2020 ; Niemi & Kousa,  2020 ; Yates et al.,  2020 ), and increased learning burdens (Niemi & Kousa,  2020 ). Although these findings provided valuable insights about the issues students experienced during online learning, information about their learning conditions and future expectations were less mentioned. Such information could assist educational authorises and institutions to better comprehend students’ difficulties and potentially improve their online learning experience. Additionally, most of these recent studies were limited to higher education, except for Yates et al. ( 2020 ) and Niemi and Kousa’s ( 2020 ) studies on senior high school students. Empirical research targeting the full spectrum of K‐12students remain scarce. Therefore, to address these gaps, the current paper reports the findings of a large‐scale study that sought to explore K‐12 students’ online learning experience during the COVID‐19 pandemic in a provincial sample of over one million Chinese students. The findings of this study provide policy recommendations to educational institutions and authorities regarding the delivery of K‐12 online education.

LITERATURE REVIEW

Learning conditions and technologies.

Having stable access to the internet is critical to students’ learning experience during online learning. Berge ( 2005 ) expressed the concern of the divide in digital‐readiness, and the pedagogical approach between different countries could influence students’ online learning experience. Digital‐readiness is the availability and adoption of information technologies and infrastructures in a country. Western countries like America (3rd) scored significantly higher in digital‐readiness compared to Asian countries like China (54th; Cisco,  2019 ). Students from low digital‐readiness countries could experience additional technology‐related problems. Supporting evidence is emerging in recent studies conducted during the COVID‐19 pandemic. In Egypt's capital city, Basuony et al. ( 2020 ) found that only around 13.9%of the students experienced issues with their internet connection. Whereas more than two‐thirds of the students in rural Indonesia reported issues of unstable internet, insufficient internet data, and incompatible learning device (Agung et al.,  2020 ).

Another influential factor for K‐12 students to adequately adapt to online learning is the accessibility of appropriate technological devices, especially having access to a desktop or a laptop (Barbour et al., 2018 ). However, it is unlikely for most of the students to satisfy this requirement. Even in higher education, around 76% of students reported having incompatible devices for online learning and only 15% of students used laptop for online learning, whereas around 85% of them used smartphone (Agung et al.,  2020 ). It is very likely that K‐12 students also suffer from this availability issue as they depend on their parents to provide access to relevant learning devices.

Technical issues surrounding technological devices could also influence students’ experience in online learning. (Barbour & Reeves,  2009 ) argues that students need to have a high level of digital literacy to find and use relevant information and communicate with others through technological devices. Students lacking this ability could experience difficulties in online learning. Bączek et al. ( 2021 ) found that around 54% of the medical students experienced technical problems with IT equipment and this issue was more prevalent in students with lower years of tertiary education. Likewise, Niemi and Kousa ( 2020 ) also find that students in a Finish high school experienced increased amounts of technical problems during the examination period, which involved additional technical applications. These findings are concerning as young children and adolescent in primary and lower secondary school could be more vulnerable to these technical problems as they are less experienced with the technologies in online learning (Barbour & LaBonte,  2017 ). Therefore, it is essential to investigate the learning conditions and the related difficulties experienced by students in K‐12 education as the extend of effects on them remain underexplored.

Learning experience and interactions

Apart from the aforementioned issues, the extent of interaction and collaborative learning opportunities available in online learning could also influence students’ experience. The literature on online learning has long emphasised the role of effective interaction for the success of student learning. According to Muirhead and Juwah ( 2004 ), interaction is an event that can take the shape of any type of communication between two or subjects and objects. Specifically, the literature acknowledges the three typical forms of interactions (Moore,  1989 ): (i) student‐content, (ii) student‐student, and (iii) student‐teacher. Anderson ( 2003 ) posits, in the well‐known interaction equivalency theorem, learning experiences will not deteriorate if only one of the three interaction is of high quality, and the other two can be reduced or even eliminated. Quality interaction can be accomplished by across two dimensions: (i) structure—pedagogical means that guide student interaction with contents or other students and (ii) dialogue—communication that happens between students and teachers and among students. To be able to scale online learning and prevent the growth of teaching costs, the emphasise is typically on structure (i.e., pedagogy) that can promote effective student‐content and student‐student interaction. The role of technology and media is typically recognised as a way to amplify the effect of pedagogy (Lou et al.,  2006 ). Novel technological innovations—for example learning analytics‐based personalised feedback at scale (Pardo et al.,  2019 ) —can also empower teachers to promote their interaction with students.

Online education can lead to a sense of isolation, which can be detrimental to student success (McInnerney & Roberts,  2004 ). Therefore, integration of social interaction into pedagogy for online learning is essential, especially at the times when students do not actually know each other or have communication and collaboration skills underdeveloped (Garrison et al.,  2010 ; Gašević et al.,  2015 ). Unfortunately, existing evidence suggested that online learning delivery during the COVID‐19 pandemic often lacks interactivity and collaborative experiences (Bączek et al.,  2021 ; Yates et al.,  2020 ). Bączek et al., ( 2021 ) found that around half of the medical students reported reduced interaction with teachers, and only 4% of students think online learning classes are interactive. Likewise, Yates et al. ( 2020 )’s study in high school students also revealed that over half of the students preferred in‐class collaboration over online collaboration as they value the immediate support and the proximity to teachers and peers from in‐class interaction.

Learning expectations and age differentiation

Although these studies have provided valuable insights and stressed the need for more interactivity in online learning, K‐12 students in different school years could exhibit different expectations for the desired activities in online learning. Piaget's Cognitive Developmental Theory illustrated children's difficulties in understanding abstract and hypothetical concepts (Thomas,  2000 ). Primary school students will encounter many abstract concepts in their STEM education (Uttal & Cohen,  2012 ). In face‐to‐face learning, teachers provide constant guidance on students’ learning progress and can help them to understand difficult concepts. Unfortunately, the level of guidance significantly drops in online learning, and, in most cases, children have to face learning obstacles by themselves (Barbour,  2013 ). Additionally, lower primary school students may lack the metacognitive skills to use various online learning functions, maintain engagement in synchronous online learning, develop and execute self‐regulated learning plans, and engage in meaningful peer interactions during online learning (Barbour,  2013 ; Broadbent & Poon,  2015 ; Huffaker & Calvert, 2003; Wang et al.,  2013 ). Thus, understanding these younger students’ expectations is imperative as delivering online learning to them in the same way as a virtual high school could hinder their learning experiences. For students with more matured metacognition, their expectations of online learning could be substantially different from younger students. Niemi et al.’s study ( 2020 ) with students in a Finish high school have found that students often reported heavy workload and fatigue during online learning. These issues could cause anxiety and reduce students’ learning motivation, which would have negative consequences on their emotional well‐being and academic performance (Niemi & Kousa,  2020 ; Yates et al.,  2020 ), especially for senior students who are under the pressure of examinations. Consequently, their expectations of online learning could be orientated toward having additional learning support functions and materials. Likewise, they could also prefer having more opportunities for peer interactions as these interactions are beneficial to their emotional well‐being and learning performance (Gašević et al., 2013 ; Montague & Rinaldi, 2001 ). Therefore, it is imperative to investigate the differences between online learning expectations in students of different school years to suit their needs better.

Research questions

By building upon the aforementioned relevant works, this study aimed to contribute to the online learning literature with a comprehensive understanding of the online learning experience that K‐12 students had during the COVID‐19 pandemic period in China. Additionally, this study also aimed to provide a thorough discussion of what potential actions can be undertaken to improve online learning delivery. Formally, this study was guided by three research questions (RQs):

RQ1 . What learning conditions were experienced by students across 12 years of education during their online learning process in the pandemic period? RQ2 . What benefits and obstacles were perceived by students across 12 years of education when performing online learning? RQ3 . What expectations do students, across 12 years of education, have for future online learning practices ?

Participants

The total number of K‐12 students in the Guangdong Province of China is around 15 million. In China, students of Year 1–6, Year 7–9, and Year 10–12 are referred to as students of primary school, middle school, and high school, respectively. Typically, students in China start their study in primary school at the age of around six. At the end of their high‐school study, students have to take the National College Entrance Examination (NCEE; also known as Gaokao) to apply for tertiary education. The survey was administrated across the whole Guangdong Province, that is the survey was exposed to all of the 15 million K‐12 students, though it was not mandatory for those students to accomplish the survey. A total of 1,170,769 students completed the survey, which accounts for a response rate of 7.80%. After removing responses with missing values and responses submitted from the same IP address (duplicates), we had 1,048,575 valid responses, which accounts to about 7% of the total K‐12 students in the Guangdong Province. The number of students in different school years is shown in Figure  1 . Overall, students were evenly distributed across different school years, except for a smaller sample in students of Year 10–12.

The number of students in each school year

Survey design

The survey was designed collaboratively by multiple relevant parties. Firstly, three educational researchers working in colleges and universities and three educational practitioners working in the Department of Education in Guangdong Province were recruited to co‐design the survey. Then, the initial draft of the survey was sent to 30 teachers from different primary and secondary schools, whose feedback and suggestions were considered to improve the survey. The final survey consisted of a total of 20 questions, which, broadly, can be classified into four categories: demographic, behaviours, experiences, and expectations. Details are available in Appendix.

All K‐12 students in the Guangdong Province were made to have full‐time online learning from March 1, 2020 after the outbreak of COVID‐19 in January in China. A province‐level online learning platform was provided to all schools by the government. In addition to the learning platform, these schools can also use additional third‐party platforms to facilitate the teaching activities, for example WeChat and Dingding, which provide services similar to WhatsApp and Zoom. The main change for most teachers was that they had to shift the classroom‐based lectures to online lectures with the aid of web‐conferencing tools. Similarly, these teachers also needed to perform homework marking and have consultation sessions in an online manner.

The Department of Education in the Guangdong Province of China distributed the survey to all K‐12 schools in the province on March 21, 2020 and collected responses on March 26, 2020. Students could access and answer the survey anonymously by either scan the Quick Response code along with the survey or click the survey address link on their mobile device. The survey was administrated in a completely voluntary manner and no incentives were given to the participants. Ethical approval was granted by the Department of Education in the Guangdong Province. Parental approval was not required since the survey was entirely anonymous and facilitated by the regulating authority, which satisfies China's ethical process.

The original survey was in Chinese, which was later translated by two bilingual researchers and verified by an external translator who is certified by the Australian National Accreditation Authority of Translators and Interpreters. The original and translated survey questionnaires are available in Supporting Information. Given the limited space we have here and the fact that not every survey item is relevant to the RQs, the following items were chosen to answer the RQs: item Q3 (learning media) and Q11 (learning approaches) for RQ1, item Q13 (perceived obstacle) and Q19 (perceived benefits) for RQ2, and item Q19 (expected learning activities) for RQ3. Cross‐tabulation based approaches were used to analyse the collected data. To scrutinise whether the differences displayed by students of different school years were statistically significant, we performed Chi‐square tests and calculated the Cramer's V to assess the strengths of the association after chi‐square had determined significance.

For the analyses, students were segmented into four categories based on their school years, that is Year 1–3, Year 4–6, Year 7–9, and Year 10–12, to provide a clear understanding of the different experiences and needs that different students had for online learning. This segmentation was based on the educational structure of Chinese schools: elementary school (Year 1–6), middle school (Year 7–9), and high school (Year 10–12). Children in elementary school can further be segmented into junior (Year 1–3) or senior (Year 4–6) students because senior elementary students in China are facing more workloads compared to junior students due to the provincial Middle School Entry Examination at the end of Year 6.

Learning conditions—RQ1

Learning media.

The Chi‐square test showed significant association between school years and students’ reported usage of learning media, χ 2 (55, N  = 1,853,952) = 46,675.38, p  < 0.001. The Cramer's V is 0.07 ( df ∗ = 5), which indicates a small‐to‐medium effect according to Cohen’s ( 1988 ) guidelines. Based on Figure  2 , we observed that an average of up to 87.39% students used smartphones to perform online learning, while only 25.43% students used computer, which suggests that smartphones, with widespread availability in China (2020), have been adopted by students for online learning. As for the prevalence of the two media, we noticed that both smartphones ( χ 2 (3, N  = 1,048,575) = 9,395.05, p < 0.001, Cramer's V  = 0.10 ( df ∗ = 1)) and computers ( χ 2 (3, N  = 1,048,575) = 11,025.58, p <.001, Cramer's V  = 0.10 ( df ∗ = 1)) were more adopted by high‐school‐year (Year 7–12) than early‐school‐year students (Year 1–6), both with a small effect size. Besides, apparent discrepancies can be observed between the usages of TV and paper‐based materials across different school years, that is early‐school‐year students reported more TV usage ( χ 2 (3, N  = 1,048,575) = 19,505.08, p <.001), with a small‐to‐medium effect size, Cramer's V  = 0.14( df ∗ = 1). High‐school‐year students (especially Year 10–12) reported more usage of paper‐based materials ( χ 2 (3, N  = 1,048,575) = 23,401.64, p < 0.001), with a small‐to‐medium effect size, Cramer's V  = 0.15( df ∗ = 1).

Learning media used by students in online learning

Learning approaches

School years is also significantly associated with the different learning approaches students used to tackle difficult concepts during online learning, χ 2 (55, N  = 2,383,751) = 58,030.74, p < 0.001. The strength of this association is weak to moderate as shown by the Cramer's V (0.07, df ∗ = 5; Cohen,  1988 ). When encountering problems related to difficult concepts, students typically chose to “solve independently by searching online” or “rewatch recorded lectures” instead of consulting to their teachers or peers (Figure  3 ). This is probably because, compared to classroom‐based education, it is relatively less convenient and more challenging for students to seek help from others when performing online learning. Besides, compared to high‐school‐year students, early‐school‐year students (Year 1–6), reported much less use of “solve independently by searching online” ( χ 2 (3, N  = 1,048,575) = 48,100.15, p <.001), with a small‐to‐medium effect size, Cramer's V  = 0.21 ( df ∗ = 1). Also, among those approaches of seeking help from others, significantly more high‐school‐year students preferred “communicating with other students” than early‐school‐year students ( χ 2 (3, N  = 1,048,575) = 81,723.37, p < 0.001), with a medium effect size, Cramer's V  = 0.28 ( df ∗ = 1).

Learning approaches used by students in online learning

Perceived benefits and obstacles—RQ2

Perceived benefits.

The association between school years and perceived benefits in online learning is statistically significant, χ 2 (66, N  = 2,716,127) = 29,534.23, p  < 0.001, and the Cramer's V (0.04, df ∗ = 6) indicates a small effect (Cohen,  1988 ). Unsurprisingly, benefits brought by the convenience of online learning are widely recognised by students across all school years (Figure  4 ), that is up to 75% of students reported that it is “more convenient to review course content” and 54% said that they “can learn anytime and anywhere” . Besides, we noticed that about 50% of early‐school‐year students appreciated the “access to courses delivered by famous teachers” and 40%–47% of high‐school‐year students indicated that online learning is “helpful to develop self‐regulation and autonomy” .

Perceived benefits of online learning reported by students

Perceived obstacles

The Chi‐square test shows a significant association between school years and students’ perceived obstacles in online learning, χ 2 (77, N  = 2,699,003) = 31,987.56, p < 0.001. This association is relatively weak as shown by the Cramer's V (0.04, df ∗ = 7; Cohen,  1988 ). As shown in Figure  5 , the biggest obstacles encountered by up to 73% of students were the “eyestrain caused by long staring at screens” . Disengagement caused by nearby disturbance was reported by around 40% of students, especially those of Year 1–3 and 10–12. Technological‐wise, about 50% of students experienced poor Internet connection during their learning process, and around 20% of students reported the “confusion in setting up the platforms” across of school years.

Perceived obstacles of online learning reported by students

Expectations for future practices of online learning – RQ3

Online learning activities.

The association between school years and students’ expected online learning activities is significant, χ 2 (66, N  = 2,416,093) = 38,784.81, p < 0.001. The Cramer's V is 0.05 ( df ∗ = 6) which suggests a small effect (Cohen,  1988 ). As shown in Figure  6 , the most expected activity for future online learning is “real‐time interaction with teachers” (55%), followed by “online group discussion and collaboration” (38%). We also observed that more early‐school‐year students expect reflective activities, such as “regular online practice examinations” ( χ 2 (3, N  = 1,048,575) = 11,644.98, p < 0.001), with a small effect size, Cramer's V  = 0.11 ( df ∗ = 1). In contrast, more high‐school‐year students expect “intelligent recommendation system …” ( χ 2 (3, N  = 1,048,575) = 15,327.00, p < 0.001), with a small effect size, Cramer's V  = 0.12 ( df ∗ = 1).

Students’ expected online learning activities

Regarding students’ learning conditions, substantial differences were observed in learning media, family dependency, and learning approaches adopted in online learning between students in different school years. The finding of more computer and smartphone usage in high‐school‐year than early‐school‐year students can probably be explained by that, with the growing abilities in utilising these media as well as the educational systems and tools which run on these media, high‐school‐year students tend to make better use of these media for online learning practices. Whereas, the differences in paper‐based materials may imply that high‐school‐year students in China have to accomplish a substantial amount of exercise, assignments, and exam papers to prepare for the National College Entrance Examination (NCEE), whose delivery was not entirely digitised due to the sudden transition to online learning. Meanwhile, high‐school‐year students may also have preferred using paper‐based materials for exam practice, as eventually, they would take their NCEE in the paper format. Therefore, these substantial differences in students’ usage of learning media should be addressed by customising the delivery method of online learning for different school years.

Other than these between‐age differences in learning media, the prevalence of smartphone in online learning resonates with Agung et al.’s ( 2020 ) finding on the issues surrounding the availability of compatible learning device. The prevalence of smartphone in K‐12 students is potentially problematic as the majority of the online learning platform and content is designed for computer‐based learning (Berge,  2005 ; Molnar et al.,  2019 ). Whereas learning with smartphones has its own unique challenges. For example, Gikas and Grant ( 2013 ) discovered that students who learn with smartphone experienced frustration with the small screen‐size, especially when trying to type with the tiny keypad. Another challenge relates to the distraction of various social media applications. Although similar distractions exist in computer and web‐based social media, the level of popularity, especially in the young generation, are much higher in mobile‐based social media (Montag et al.,  2018 ). In particular, the message notification function in smartphones could disengage students from learning activities and allure them to social media applications (Gikas & Grant,  2013 ). Given these challenges of learning with smartphones, more research efforts should be devoted to analysing students’ online learning behaviour in the setting of mobile learning to accommodate their needs better.

The differences in learning approaches, once again, illustrated that early‐school‐year students have different needs compared to high‐school‐year students. In particular, the low usage of the independent learning methods in early‐school‐year students may reflect their inability to engage in independent learning. Besides, the differences in help seeking behaviours demonstrated the distinctive needs for communication and interaction between different students, that is early‐school‐year students have a strong reliance on teachers and high‐school‐year students, who are equipped with stronger communication ability, are more inclined to interact with their peers. This finding implies that the design of online learning platforms should take students’ different needs into account. Thus, customisation is urgently needed for the delivery of online learning to different school years.

In terms of the perceived benefits and challenges of online learning, our results resonate with several previous findings. In particular, the benefits of convenience are in line with the flexibility advantages of online learning, which were mentioned in prior works (Appana,  2008 ; Bączek et al.,  2021 ; Barbour,  2013 ; Basuony et al.,  2020 ; Harvey et al.,  2014 ). Early‐school‐year students’ higher appreciation in having “access to courses delivered by famous teachers” and lower appreciation in the independent learning skills developed through online learning are also in line with previous literature (Barbour,  2013 ; Harvey et al.,  2014 ; Oliver et al.,  2009 ). Again, these similar findings may indicate the strong reliance that early‐school‐year students place on teachers, while high‐school‐year students are more capable of adapting to online learning by developing independent learning skills.

Technology‐wise, students’ experience of poor internet connection and confusion in setting up online learning platforms are particularly concerning. The problem of poor internet connection corroborated the findings reported in prior studies (Agung et al.,  2020 ; Barbour,  2013 ; Basuony et al.,  2020 ; Berge,  2005 ; Rice,  2006 ), that is the access issue surrounded the digital divide as one of the main challenges of online learning. In the era of 4G and 5G networks, educational authorities and institutions that deliver online education could fall into the misconception of most students have a stable internet connection at home. The internet issue we observed is particularly vital to students’ online learning experience as most students prefer real‐time communications (Figure  6 ), which rely heavily on stable internet connection. Likewise, the finding of students’ confusion in technology is also consistent with prior studies (Bączek et al.,  2021 ; Muilenburg & Berge,  2005 ; Niemi & Kousa,  2020 ; Song et al.,  2004 ). Students who were unsuccessfully in setting up the online learning platforms could potentially experience declines in confidence and enthusiasm for online learning, which would cause a subsequent unpleasant learning experience. Therefore, both the readiness of internet infrastructure and student technical skills remain as the significant challenges for the mass‐adoption of online learning.

On the other hand, students’ experience of eyestrain from extended screen time provided empirical evidence to support Spitzer’s ( 2001 ) speculation about the potential ergonomic impact of online learning. This negative effect is potentially related to the prevalence of smartphone device and the limited screen size of these devices. This finding not only demonstrates the potential ergonomic issues that would be caused by smartphone‐based online learning but also resonates with the aforementioned necessity of different platforms and content designs for different students.

A less‐mentioned problem in previous studies on online learning experiences is the disengagement caused by nearby disturbance, especially in Year 1–3 and 10–12. It is likely that early‐school‐year students suffered from this problem because of their underdeveloped metacognitive skills to concentrate on online learning without teachers’ guidance. As for high‐school‐year students, the reasons behind their disengagement require further investigation in the future. Especially it would be worthwhile to scrutinise whether this type of disengagement is caused by the substantial amount of coursework they have to undertake and the subsequent a higher level of pressure and a lower level of concentration while learning.

Across age‐level differences are also apparent in terms of students’ expectations of online learning. Although, our results demonstrated students’ needs of gaining social interaction with others during online learning, findings (Bączek et al.,  2021 ; Harvey et al.,  2014 ; Kuo et al.,  2014 ; Liu & Cavanaugh,  2012 ; Yates et al.,  2020 ). This need manifested differently across school years, with early‐school‐year students preferring more teacher interactions and learning regulation support. Once again, this finding may imply that early‐school‐year students are inadequate in engaging with online learning without proper guidance from their teachers. Whereas, high‐school‐year students prefer more peer interactions and recommendation to learning resources. This expectation can probably be explained by the large amount of coursework exposed to them. Thus, high‐school‐year students need further guidance to help them better direct their learning efforts. These differences in students’ expectations for future practices could guide the customisation of online learning delivery.

Implications

As shown in our results, improving the delivery of online learning not only requires the efforts of policymakers but also depend on the actions of teachers and parents. The following sub‐sections will provide recommendations for relevant stakeholders and discuss their essential roles in supporting online education.

Technical support

The majority of the students has experienced technical problems during online learning, including the internet lagging and confusion in setting up the learning platforms. These problems with technology could impair students’ learning experience (Kauffman,  2015 ; Muilenburg & Berge,  2005 ). Educational authorities and schools should always provide a thorough guide and assistance for students who are experiencing technical problems with online learning platforms or other related tools. Early screening and detection could also assist schools and teachers to direct their efforts more effectively in helping students with low technology skills (Wilkinson et al.,  2010 ). A potential identification method involves distributing age‐specific surveys that assess students’ Information and Communication Technology (ICT) skills at the beginning of online learning. For example, there are empirical validated ICT surveys available for both primary (Aesaert et al.,  2014 ) and high school (Claro et al.,  2012 ) students.

For students who had problems with internet lagging, the delivery of online learning should provide options that require fewer data and bandwidth. Lecture recording is the existing option but fails to address students’ need for real‐time interaction (Clark et al.,  2015 ; Malik & Fatima,  2017 ). A potential alternative involves providing students with the option to learn with digital or physical textbooks and audio‐conferencing, instead of screen sharing and video‐conferencing. This approach significantly reduces the amount of data usage and lowers the requirement of bandwidth for students to engage in smooth online interactions (Cisco,  2018 ). It also requires little additional efforts from teachers as official textbooks are often available for each school year, and thus, they only need to guide students through the materials during audio‐conferencing. Educational authority can further support this approach by making digital textbooks available for teachers and students, especially those in financial hardship. However, the lack of visual and instructor presence could potentially reduce students’ attention, recall of information, and satisfaction in online learning (Wang & Antonenko,  2017 ). Therefore, further research is required to understand whether the combination of digital or physical textbooks and audio‐conferencing is appropriate for students with internet problems. Alternatively, suppose the local technological infrastructure is well developed. In that case, governments and schools can also collaborate with internet providers to issue data and bandwidth vouchers for students who are experiencing internet problems due to financial hardship.

For future adoption of online learning, policymakers should consider the readiness of the local internet infrastructure. This recommendation is particularly important for developing countries, like Bangladesh, where the majority of the students reported the lack of internet infrastructure (Ramij & Sultana,  2020 ). In such environments, online education may become infeasible, and alternative delivery method could be more appropriate, for example, the Telesecundaria program provides TV education for rural areas of Mexico (Calderoni,  1998 ).

Other than technical problems, choosing a suitable online learning platform is also vital for providing students with a better learning experience. Governments and schools should choose an online learning platform that is customised for smartphone‐based learning, as the majority of students could be using smartphones for online learning. This recommendation is highly relevant for situations where students are forced or involuntarily engaged in online learning, like during the COVID‐19 pandemic, as they might not have access to a personal computer (Molnar et al.,  2019 ).

Customisation of delivery methods

Customising the delivery of online learning for students in different school years is the theme that appeared consistently across our findings. This customisation process is vital for making online learning an opportunity for students to develop independent learning skills, which could help prepare them for tertiary education and lifelong learning. However, the pedagogical design of K‐12 online learning programs should be differentiated from adult‐orientated programs as these programs are designed for independent learners, which is rarely the case for students in K‐12 education (Barbour & Reeves,  2009 ).

For early‐school‐year students, especially Year 1–3 students, providing them with sufficient guidance from both teachers and parents should be the priority as these students often lack the ability to monitor and reflect on learning progress. In particular, these students would prefer more real‐time interaction with teachers, tutoring from parents, and regular online practice examinations. These forms of guidance could help early‐school‐year students to cope with involuntary online learning, and potentially enhance their experience in future online learning. It should be noted that, early‐school‐year students demonstrated interest in intelligent monitoring and feedback systems for learning. Additional research is required to understand whether these young children are capable of understanding and using learning analytics that relay information on their learning progress. Similarly, future research should also investigate whether young children can communicate effectively through digital tools as potential inability could hinder student learning in online group activities. Therefore, the design of online learning for early‐school‐year students should focus less on independent learning but ensuring that students are learning effective under the guidance of teachers and parents.

In contrast, group learning and peer interaction are essential for older children and adolescents. The delivery of online learning for these students should focus on providing them with more opportunities to communicate with each other and engage in collaborative learning. Potential methods to achieve this goal involve assigning or encouraging students to form study groups (Lee et al.,  2011 ), directing students to use social media for peer communication (Dabbagh & Kitsantas,  2012 ), and providing students with online group assignments (Bickle & Rucker,  2018 ).

Special attention should be paid to students enrolled in high schools. For high‐school‐year students, in particular, students in Year 10–12, we also recommend to provide them with sufficient access to paper‐based learning materials, such as revision booklet and practice exam papers, so they remain familiar with paper‐based examinations. This recommendation applies to any students who engage in online learning but has to take their final examination in paper format. It is also imperative to assist high‐school‐year students who are facing examinations to direct their learning efforts better. Teachers can fulfil this need by sharing useful learning resources on the learning management system, if it is available, or through social media groups. Alternatively, students are interested in intelligent recommendation systems for learning resources, which are emerging in the literature (Corbi & Solans,  2014 ; Shishehchi et al.,  2010 ). These systems could provide personalised recommendations based on a series of evaluation on learners’ knowledge. Although it is infeasible for situations where the transformation to online learning happened rapidly (i.e., during the COVID‐19 pandemic), policymakers can consider embedding such systems in future online education.

Limitations

The current findings are limited to primary and secondary Chinese students who were involuntarily engaged in online learning during the COVID‐19 pandemic. Despite the large sample size, the population may not be representative as participants are all from a single province. Also, information about the quality of online learning platforms, teaching contents, and pedagogy approaches were missing because of the large scale of our study. It is likely that the infrastructures of online learning in China, such as learning platforms, instructional designs, and teachers’ knowledge about online pedagogy, were underprepared for the sudden transition. Thus, our findings may not represent the experience of students who voluntarily participated in well‐prepared online learning programs, in particular, the virtual school programs in America and Canada (Barbour & LaBonte,  2017 ; Molnar et al.,  2019 ). Lastly, the survey was only evaluated and validated by teachers but not students. Therefore, students with the lowest reading comprehension levels might have a different understanding of the items’ meaning, especially terminologies that involve abstract contracts like self‐regulation and autonomy in item Q17.

In conclusion, we identified across‐year differences between primary and secondary school students’ online learning experience during the COVID‐19 pandemic. Several recommendations were made for the future practice and research of online learning in the K‐12 student population. First, educational authorities and schools should provide sufficient technical support to help students to overcome potential internet and technical problems, as well as choosing online learning platforms that have been customised for smartphones. Second, customising the online pedagogy design for students in different school years, in particular, focusing on providing sufficient guidance for young children, more online collaborative opportunity for older children and adolescent, and additional learning resource for senior students who are facing final examinations.

CONFLICT OF INTEREST

There is no potential conflict of interest in this study.

ETHICS STATEMENT

The data are collected by the Department of Education of the Guangdong Province who also has the authority to approve research studies in K12 education in the province.

Supporting information

Supplementary Material

ACKNOWLEDGEMENTS

This work is supported by the National Natural Science Foundation of China (62077028, 61877029), the Science and Technology Planning Project of Guangdong (2020B0909030005, 2020B1212030003, 2020ZDZX3013, 2019B1515120010, 2018KTSCX016, 2019A050510024), the Science and Technology Planning Project of Guangzhou (201902010041), and the Fundamental Research Funds for the Central Universities (21617408, 21619404).

SURVEY ITEMS

Yan, L , Whitelock‐Wainwright, A , Guan, Q , Wen, G , Gašević, D , & Chen, G . Students’ experience of online learning during the COVID‐19 pandemic: A province‐wide survey study . Br J Educ Technol . 2021; 52 :2038–2057. 10.1111/bjet.13102 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

DATA AVAILABILITY STATEMENT

• Aesaert, K. , Van Nijlen, D. , Vanderlinde, R. , & van Braak, J. (2014). Direct measures of digital information processing and communication skills in primary education: Using item response theory for the development and validation of an ICT competence scale . Computers & Education , 76 , 168–181. 10.1016/j.compedu.2014.03.013 [ CrossRef ] [ Google Scholar ]
• Agung, A. S. N. , Surtikanti, M. W. , & Quinones, C. A. (2020). Students’ perception of online learning during COVID‐19 pandemic: A case study on the English students of STKIP Pamane Talino . SOSHUM: Jurnal Sosial Dan Humaniora , 10 ( 2 ), 225–235. 10.31940/soshum.v10i2.1316 [ CrossRef ] [ Google Scholar ]
• Anderson, T. (2003). Getting the mix right again: An updated and theoretical rationale for interaction . The International Review of Research in Open and Distributed Learning , 4 ( 2 ). 10.19173/irrodl.v4i2.149 [ CrossRef ] [ Google Scholar ]
• Appana, S. (2008). A review of benefits and limitations of online learning in the context of the student, the instructor and the tenured faculty . International Journal on E‐learning , 7 ( 1 ), 5–22. [ Google Scholar ]
• Bączek, M. , Zagańczyk‐Bączek, M. , Szpringer, M. , Jaroszyński, A. , & Wożakowska‐Kapłon, B. (2021). Students’ perception of online learning during the COVID‐19 pandemic: A survey study of Polish medical students . Medicine , 100 ( 7 ), e24821. 10.1097/MD.0000000000024821 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Barbour, M. K. (2013). The landscape of k‐12 online learning: Examining what is known . Handbook of Distance Education , 3 , 574–593. [ Google Scholar ]
• Barbour, M. , Huerta, L. , & Miron, G. (2018). Virtual schools in the US: Case studies of policy, performance and research evidence. In Society for information technology & teacher education international conference (pp. 672–677). Association for the Advancement of Computing in Education (AACE). [ Google Scholar ]
• Barbour, M. K. , & LaBonte, R. (2017). State of the nation: K‐12 e‐learning in Canada, 2017 edition . http://k12sotn.ca/wp‐content/uploads/2018/02/StateNation17.pdf [ Google Scholar ]
• Barbour, M. K. , & Reeves, T. C. (2009). The reality of virtual schools: A review of the literature . Computers & Education , 52 ( 2 ), 402–416. [ Google Scholar ]
• Basuony, M. A. K. , EmadEldeen, R. , Farghaly, M. , El‐Bassiouny, N. , & Mohamed, E. K. A. (2020). The factors affecting student satisfaction with online education during the COVID‐19 pandemic: An empirical study of an emerging Muslim country . Journal of Islamic Marketing . 10.1108/JIMA-09-2020-0301 [ CrossRef ] [ Google Scholar ]
• Berge, Z. L. (2005). Virtual schools: Planning for success . Teachers College Press, Columbia University. [ Google Scholar ]
• Bickle, M. C. , & Rucker, R. (2018). Student‐to‐student interaction: Humanizing the online classroom using technology and group assignments . Quarterly Review of Distance Education , 19 ( 1 ), 1–56. [ Google Scholar ]
• Broadbent, J. , & Poon, W. L. (2015). Self‐regulated learning strategies & academic achievement in online higher education learning environments: A systematic review . The Internet and Higher Education , 27 , 1–13. [ Google Scholar ]
• Calderoni, J. (1998). Telesecundaria: Using TV to bring education to rural Mexico (Tech. Rep.). The World Bank. [ Google Scholar ]
• Cisco . (2018). Bandwidth requirements for meetings with cisco Webex and collaboration meeting rooms white paper . http://dwz.date/dpbc [ Google Scholar ]
• Cisco . (2019). Cisco digital readiness 2019 . https://www.cisco.com/c/m/en_us/about/corporate‐social‐responsibility/research‐resources/digital‐readiness‐index.html#/ (Library Catalog: www.cisco.com). [ Google Scholar ]
• Clark, C. , Strudler, N. , & Grove, K. (2015). Comparing asynchronous and synchronous video vs. text based discussions in an online teacher education course . Online Learning , 19 ( 3 ), 48–69. [ Google Scholar ]
• Claro, M. , Preiss, D. D. , San Martín, E. , Jara, I. , Hinostroza, J. E. , Valenzuela, S. , Cortes, F. , & Nussbaum, M. (2012). Assessment of 21st century ICT skills in Chile: Test design and results from high school level students . Computers & Education , 59 ( 3 ), 1042–1053. 10.1016/j.compedu.2012.04.004 [ CrossRef ] [ Google Scholar ]
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences . Routledge Academic. [ Google Scholar ]
• Corbi, A. , & Solans, D. B. (2014). Review of current student‐monitoring techniques used in elearning‐focused recommender systems and learning analytics: The experience API & LIME model case study . IJIMAI , 2 ( 7 ), 44–52. [ Google Scholar ]
• Dabbagh, N. , & Kitsantas, A. (2012). Personal learning environments, social media, and self‐regulated learning: A natural formula for connecting formal and informal learning . The Internet and Higher Education , 15 ( 1 ), 3–8. 10.1016/j.iheduc.2011.06.002 [ CrossRef ] [ Google Scholar ]
• Garrison, D. R. , Cleveland‐Innes, M. , & Fung, T. S. (2010). Exploring causal relationships among teaching, cognitive and social presence: Student perceptions of the community of inquiry framework . The Internet and Higher Education , 13 ( 1–2 ), 31–36. 10.1016/j.iheduc.2009.10.002 [ CrossRef ] [ Google Scholar ]
• Gašević, D. , Adesope, O. , Joksimović, S. , & Kovanović, V. (2015). Externally‐facilitated regulation scaffolding and role assignment to develop cognitive presence in asynchronous online discussions . The Internet and Higher Education , 24 , 53–65. 10.1016/j.iheduc.2014.09.006 [ CrossRef ] [ Google Scholar ]
• Gašević, D. , Zouaq, A. , & Janzen, R. (2013). “Choose your classmates, your GPA is at stake!” The association of cross‐class social ties and academic performance . American Behavioral Scientist , 57 ( 10 ), 1460–1479. [ Google Scholar ]
• Gikas, J. , & Grant, M. M. (2013). Mobile computing devices in higher education: Student perspectives on learning with cellphones, smartphones & social media . The Internet and Higher Education , 19 , 18–26. [ Google Scholar ]
• Harvey, D. , Greer, D. , Basham, J. , & Hu, B. (2014). From the student perspective: Experiences of middle and high school students in online learning . American Journal of Distance Education , 28 ( 1 ), 14–26. 10.1080/08923647.2014.868739 [ CrossRef ] [ Google Scholar ]
• Kauffman, H. (2015). A review of predictive factors of student success in and satisfaction with online learning . Research in Learning Technology , 23 . 10.3402/rlt.v23.26507 [ CrossRef ] [ Google Scholar ]
• Kuo, Y.‐C. , Walker, A. E. , Belland, B. R. , Schroder, K. E. , & Kuo, Y.‐T. (2014). A case study of integrating interwise: Interaction, internet self‐efficacy, and satisfaction in synchronous online learning environments . International Review of Research in Open and Distributed Learning , 15 ( 1 ), 161–181. 10.19173/irrodl.v15i1.1664 [ CrossRef ] [ Google Scholar ]
• Lee, S. J. , Srinivasan, S. , Trail, T. , Lewis, D. , & Lopez, S. (2011). Examining the relationship among student perception of support, course satisfaction, and learning outcomes in online learning . The Internet and Higher Education , 14 ( 3 ), 158–163. 10.1016/j.iheduc.2011.04.001 [ CrossRef ] [ Google Scholar ]
• Liu, F. , & Cavanaugh, C. (2012). Factors influencing student academic performance in online high school algebra . Open Learning: The Journal of Open, Distance and e‐Learning , 27 ( 2 ), 149–167. 10.1080/02680513.2012.678613 [ CrossRef ] [ Google Scholar ]
• Lou, Y. , Bernard, R. M. , & Abrami, P. C. (2006). Media and pedagogy in undergraduate distance education: A theory‐based meta‐analysis of empirical literature . Educational Technology Research and Development , 54 ( 2 ), 141–176. 10.1007/s11423-006-8252-x [ CrossRef ] [ Google Scholar ]
• Malik, M. , & Fatima, G. (2017). E‐learning: Students’ perspectives about asynchronous and synchronous resources at higher education level . Bulletin of Education and Research , 39 ( 2 ), 183–195. [ Google Scholar ]
• McInnerney, J. M. , & Roberts, T. S. (2004). Online learning: Social interaction and the creation of a sense of community . Journal of Educational Technology & Society , 7 ( 3 ), 73–81. [ Google Scholar ]
• Molnar, A. , Miron, G. , Elgeberi, N. , Barbour, M. K. , Huerta, L. , Shafer, S. R. , & Rice, J. K. (2019). Virtual schools in the US 2019 . National Education Policy Center. [ Google Scholar ]
• Montague, M. , & Rinaldi, C. (2001). Classroom dynamics and children at risk: A followup . Learning Disability Quarterly , 24 ( 2 ), 75–83. [ Google Scholar ]
• Montag, C. , Becker, B. , & Gan, C. (2018). The multipurpose application Wechat: A review on recent research . Frontiers in Psychology , 9 , 2247. 10.3389/fpsyg.2018.02247 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Moore, M. G. (1989). Editorial: Three types of interaction . American Journal of Distance Education , 3 ( 2 ), 1–7. 10.1080/08923648909526659 [ CrossRef ] [ Google Scholar ]
• Muilenburg, L. Y. , & Berge, Z. L. (2005). Student barriers to online learning: A factor analytic study . Distance Education , 26 ( 1 ), 29–48. 10.1080/01587910500081269 [ CrossRef ] [ Google Scholar ]
• Muirhead, B. , & Juwah, C. (2004). Interactivity in computer‐mediated college and university education: A recent review of the literature . Journal of Educational Technology & Society , 7 ( 1 ), 12–20. [ Google Scholar ]
• Niemi, H. M. , & Kousa, P. (2020). A case study of students’ and teachers’ perceptions in a finnish high school during the COVID pandemic . International Journal of Technology in Education and Science , 4 ( 4 ), 352–369. 10.46328/ijtes.v4i4.167 [ CrossRef ] [ Google Scholar ]
• Oliver, K. , Osborne, J. , & Brady, K. (2009). What are secondary students’ expectations for teachers in virtual school environments? Distance Education , 30 ( 1 ), 23–45. 10.1080/01587910902845923 [ CrossRef ] [ Google Scholar ]
• Pardo, A. , Jovanovic, J. , Dawson, S. , Gašević, D. , & Mirriahi, N. (2019). Using learning analytics to scale the provision of personalised feedback . British Journal of Educational Technology , 50 ( 1 ), 128–138. 10.1111/bjet.12592 [ CrossRef ] [ Google Scholar ]
• Ramij, M. , & Sultana, A. (2020). Preparedness of online classes in developing countries amid covid‐19 outbreak: A perspective from Bangladesh. Afrin, Preparedness of Online Classes in Developing Countries amid COVID‐19 Outbreak: A Perspective from Bangladesh (June 29, 2020) .
• Rice, K. L. (2006). A comprehensive look at distance education in the k–12 context . Journal of Research on Technology in Education , 38 ( 4 ), 425–448. 10.1080/15391523.2006.10782468 [ CrossRef ] [ Google Scholar ]
• Shishehchi, S. , Banihashem, S. Y. , & Zin, N. A. M. (2010). A proposed semantic recommendation system for elearning: A rule and ontology based e‐learning recommendation system. In 2010 international symposium on information technology (Vol. 1, pp. 1–5).
• Song, L. , Singleton, E. S. , Hill, J. R. , & Koh, M. H. (2004). Improving online learning: Student perceptions of useful and challenging characteristics . The Internet and Higher Education , 7 ( 1 ), 59–70. 10.1016/j.iheduc.2003.11.003 [ CrossRef ] [ Google Scholar ]
• Spitzer, D. R. (2001). Don’t forget the high‐touch with the high‐tech in distance learning . Educational Technology , 41 ( 2 ), 51–55. [ Google Scholar ]
• Thomas, R. M. (2000). Comparing theories of child development. Wadsworth/Thomson Learning. United Nations Educational, Scientific and Cultural Organization. (2020, March). Education: From disruption to recovery . https://en.unesco.org/covid19/educationresponse (Library Catalog: en.unesco.org)
• Uttal, D. H. , & Cohen, C. A. (2012). Spatial thinking and stem education: When, why, and how? In Psychology of learning and motivation (Vol. 57 , pp. 147–181). Elsevier. [ Google Scholar ]
• Van Lancker, W. , & Parolin, Z. (2020). Covid‐19, school closures, and child poverty: A social crisis in the making . The Lancet Public Health , 5 ( 5 ), e243–e244. 10.1016/S2468-2667(20)30084-0 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wang, C.‐H. , Shannon, D. M. , & Ross, M. E. (2013). Students’ characteristics, self‐regulated learning, technology self‐efficacy, and course outcomes in online learning . Distance Education , 34 ( 3 ), 302–323. 10.1080/01587919.2013.835779 [ CrossRef ] [ Google Scholar ]
• Wang, J. , & Antonenko, P. D. (2017). Instructor presence in instructional video: Effects on visual attention, recall, and perceived learning . Computers in Human Behavior , 71 , 79–89. 10.1016/j.chb.2017.01.049 [ CrossRef ] [ Google Scholar ]
• Wilkinson, A. , Roberts, J. , & While, A. E. (2010). Construction of an instrument to measure student information and communication technology skills, experience and attitudes to e‐learning . Computers in Human Behavior , 26 ( 6 ), 1369–1376. 10.1016/j.chb.2010.04.010 [ CrossRef ] [ Google Scholar ]
• World Health Organization . (2020, July). Coronavirus disease 2019 (COVID‐19): Situation Report‐164 (Situation Report No. 164). https://www.who.int/docs/default‐source/coronaviruse/situation‐reports/20200702‐covid‐19‐sitrep‐164.pdf?sfvrsn$=$ac074f58$_$2
• Yates, A. , Starkey, L. , Egerton, B. , & Flueggen, F. (2020). High school students’ experience of online learning during Covid‐19: The influence of technology and pedagogy . Technology, Pedagogy and Education , 9 , 1–15. 10.1080/1475939X.2020.1854337 [ CrossRef ] [ Google Scholar ]

Essays About Online Class: Top 5 Examples and 7 Prompts

Essays about online class tell many stories. If you need to write about e-learning, discover the top examples and prompts for the subject in our guide.

With over 5.8 million American students attending in 2021, online classes are now one of the education sector’s most popular and modern learning methods. Although it became prevalent because of the pandemic, it’s believed that the concept of distance learning began in the late 1800s .

Online classes pose many benefits that many still take advantage of even after the pandemic. However, not everyone adjusts well to this technology-centered learning due to no face-to-face contact and difficulty learning without the back-and-forth of lesson question time. 

1. My Experience as an Online Student by Debra Sicard

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“I am not a traditional student, so I have non-traditional needs… online classes fit my lifestyle.”

Sicard shares her positive experience with online classes, primarily centering her essay on convenience. She says that with online courses, she can fit more lessons into her schedule, save her money on gas, and have more time with her family. In addition, she mentions she can work and do other things besides taking her credits.

To have a proper perspective of the topic, Sicard also includes the disadvantages of virtual learning, such as devices catching viruses and missing in-real-life interactions with her professors and classmates. But, she believes that an online student can learn as much or even more than what students learn in traditional classes.

“In an online class, a student can only achieve success if he is committed to time management, balancing personal obligations, finding an ideal study environment, asking questions, and applying more effort to completing the course requirements.”

This essay contains steps a non-traditional student can take to avoid failing online classes. The author says that students, especially multitaskers, must know how to manage and balance their time to avoid losing focus. In addition, having a dedicated study spot is necessary to avoid distractions.

“Online classes or online method of learning presents an easy and comfortable method to achieve knowledge. Online classes have now become a great alternative to traditional classes.”

The writer delves into the benefits and drawbacks of online versus traditional learning. Virtual classes offer students freedom regarding their schedules and whereabouts. Some schools also allow students to learn for free. E-learning effectively trains individuals to be responsible and disciplined. 

However, individuals who are not computer literate will find online classes frustrating. Plus, electronic devices can be bad for health, and a lack of personal interaction can hinder personality development.

“[Online course] will also help you become more self-motivated, a trait that will make you stand out in the workplace and beyond.”

By listing the similarities and differences between online and traditional schools, the author demonstrates what classes a student should pick. The writer concludes that while traditional schools prepare students for the real world by interacting with diverse people, online schools help students become more self-motivated to stand out.

“The advantages of online classes take over their disadvantages. If students want to learn, then they have immense opportunities to learn from online classes.”

The author defines online classes as a type of education system where students use electronic devices with an internet connection to learn. However, while online learning improves the quality of education, it can also make the student lazy and cultivates a sense of isolation. Ultimately, they believe that to have the best education system, school teachers and officials must learn how to combine the two methods.

If the topic you’re thinking of is still confusing and you don’t know where to start, here are seven easy writing prompts to inspire you:

Explain the topic to your reader and give a brief history of the origins of online classes. Then briefly compare it to the traditional class to make the differences clear. Finally, point out the distinct features of online classes that conventional learning doesn’t offer, such as face-to-face interaction and question-and-answer debates. You can also discuss various online classes schools offer, such as hybrid learning, interactive online courses, etc.

Tell your story if you’re a student with experience with online classes. Narrate how your school switched to virtual classrooms. Relay the challenges you encountered, including how you adapted. Finish your essay by stating your current preference and why. 

For example, you favor e-learning because it cuts your transportation expenses, helps you be more responsible for managing your time, and lets you sleep in the mornings.

Relate your experience when your school moved online. Discuss any equipment or devices you need to buy before enrolling in your online class. Explain how your school handles online courses and what it does when there are technical difficulties. Add how these challenges (such as unstable internet connection and sudden power outage), such as attendance and participation, impact a class.

To make your essay more intriguing, add the average price of your online classes and if you think it’s fair. For instance, you can argue that since schools don’t provide computers and save expenses on cleaning and utilities when physical classrooms are unused, they should cut their laboratory or miscellaneous fees. You may also be interested in these articles about back to school .

Zoom, Google Classroom, and Microsoft Team are just three of the most popular online teaching software for online classes. In this prompt, look for the most useful and efficient software sites teachers or schools should incorporate into e-learning. Find examples or reliable data that show the number of students or schools that use them. Finally, ensure the details you add are accurate to make your essay credible.

Do you want to write about technology instead? Check out our  essays about technology .

Discussing online classes’ positive and negative effects is a usual essay topic. To make your essay stand out, pick the most impactful points on everyone involved. Don’t just explore the students’ perspectives. Include how virtual learning influences teachers, parents, and businesses.

To give you an idea, you can look into businesses near the campus that closed down when the school shifted to virtual classrooms.

This prompt requires you to search for publications about online classes and share your opinion on them.

For example, John F. Lyons’s book, How to Succeed in an Online Class , published in 2011, introduced technology students encounter in online classes. Suppose you read this book. First, enumerate Lyons’ advice, tips, and learning techniques to prevent a student from failing their online course. Then, briefly explain them individually and include examples or proof that his advice helped.

Online schooling has been around for a long time but has only become widespread because of the pandemic. Use this prompt to write your opinion on whether schools should make virtual learning a permanent option for students. Whatever your answer is, explain your reason to your readers.If you’re interested in learning more about essays, check out our essay writing tips !

Maria Caballero is a freelance writer who has been writing since high school. She believes that to be a writer doesn't only refer to excellent syntax and semantics but also knowing how to weave words together to communicate to any reader effectively.

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Does this course involve tests and/or exams?

No. As a flexible mini-course, there are no tests or exams. Please consider our paid courses if you are looking for an assessed course.

Can I get a certificate of completion?

No. Since the mini-course is completely flexible and there are no tests/assessments, we cannot issue a certificate of completion. If you’re looking for a certificate program, please consider our paid courses .

Can I access the templates without doing the course?

Yes. You can access the templates here .

Is this the same as the "Work Smarter Not Harder" ebook?

We unbundled the ebook a few years ago to make the content more accessible and digestible for first-time researchers. This course draws on much of the original content and is far more comprehensive than the ebook.

Need hands-on help?

If you need personalised support for your research project, consider our private coaching service.   As your research partner, we’ll hold your hand throughout the research journey, step by step .