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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.

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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

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Online classes versus traditional classes? Comparison during COVID-19

Sanjana kumari.

Department of Microbiology, All India Institute of Medical Sciences, New Delhi, India

Hitender Gautam

Neha nityadarshini, bimal kumar das, rama chaudhry, background:.

Nowadays, the use of Internet with e-learning resources anytime and anywhere leads to interaction possibilities among teachers and students from different parts of the world. It is becoming increasingly pertinent that we exploit the Internet technologies to achieve the most benefits in the education.

MATERIALS AND METHODS:

This study compares the difference between traditional classroom and e-learning in the educational environment. Medical undergraduate students of our institution were enrolled to compare between the online versus traditional method of teaching through questionnaire.

Forty percent of students found the online lecture material difficult to understand. 42.6% of respondents found it difficult to clear the doubts in online teaching; 64.4% of the participants believed that they have learned more in a face-to-face learning.

CONCLUSION:

In this study, we concluded that online mode offers flexibility on timing and delivery. Students can even download the content, notes, and assignment. Despite all the advantages offered, there is a general consensus that no technology can replace face-to-face teaching in real because in this, there will be visual as well as verbal discussion. Looking at the uncertainty of the current scenario, it is difficult to predict how long online classes will have to continue. Hence, it is of paramount importance that we assess the effectiveness of online classes and consequently take measures to ensure proper delivery of content to students, especially in a skilled field like medicine, so we concluded that face-to-face learning is of utmost importance in medical institutions.

Introduction

In these current times of information technology, students in higher education depend on a computer to do most of the work. Most higher educational institutions are also aware that using network technology can create, foster, deliver, and facilitate learning and enhance students’ experience and knowledge. Hence, the rapid developments and growth of information and communication technology have had a profound influence on higher education. E-learning means that teachers and students perform and complete the task through Internet, a method that is relatively different from traditional classroom.[ 1 ] According to a report published in 2011, over 6.1 million students were taking at least one or more online courses in 2010, with 31% of all students involved in higher education being taking at least one online course. In a more recent report, the number had increased by approximately 570,000 for a total of million students taking at least one or more online course. The report further shows and predicts that the number of students taking at least one online course is at its highest level, with the current growth rate of 9.3%, and shows no evidence of the trend slowing in the foreseeable future.[ 2 ] This trend has left many questions that need to be answered regarding what factors are driving this shift and how this shift will ultimately affect institutions across the country.

The history of online learning is particularly interesting because it not only shows the contributions of individuals but also institutions to the advancement of education and the sharing of that knowledge and skills on a global scale. As we briefly review the historical development of this subject, it is important to indicate that many authors use the terms “distance learning,” “distance education,” “online learning,” and “online education” interchangeably,[ 3 ] as is the case in this paper.

Online courses are courses where at least 80% of the content is delivered online without face-to-face meetings, whereas face-to-face instructions are a learning method where all content is delivered only in a traditional face-to-face setting.

Hybrid courses, on the other hand, combine the benefits of face-to-face learning with the technology often used in online courses. 30%–79% of the course is delivered online.

Web-facilitated courses are the ones where 1%–29% of the course is delivered online. Although this type of course is actually a face-to-face course, it uses a web-based technology to supplement the face-to-face instruction provided to students.

This study is to compare the effectiveness of a medical undergraduate, online microbiology course to a traditional in-class lecture course taught by the same instructor as measured by response to the pre-formed questionnaire.

Materials and Methods

Study design and setting.

It was a prospective study. Study participants were provided with a questionnaire to do comparison between the online versus traditional method of education. Due to COVID-19 pandemic restrictions, traditional classroom teaching was shifted to online teaching. In traditional classroom setting, lecture duration ranged typically from 45 min to 1 h, with few minutes dedicated for doubt clearing or discussion at the end. Course content was delivered by the faculty verbally, assisted by projected PowerPoint presentations. In contrast to this, course content in online mode was delivered through streaming/video-conferencing software. Students could access it through their electronic devices: phones, tablets, and laptops via a link. The content for both educational modes—online and traditional classroom-based—was identical as it was taught by the same teaching faculty. Duration of lectures remained the same as well.

Study participants and sampling

Medical undergraduate students at the Department of Microbiology, All India Institute of Medical Sciences, New Delhi, India, were the study participants. Students were the same for both modes of teaching. Out of a total strength of 101, 75 students participated.

Data collection tool and technique

A questionnaire was designed covering questions such as whether online classes provide better understanding of course content, is it easier to pay attention to lectures in online classes, whether online classes are convenient to attend, is it easier to clear doubts through online discussions, do the students face technical issues during online classes, are the students more likely to attend online classes than traditional classes, is it easier to get distracted during online classes than during traditional classes, are the students more likely to stick to the time table of traditional classes as compared to online classes, do the students miss social interaction with peers and teachers in case of online classes, and do lack of face to face communication makes online classes less engaging.

Printed copies of the questionnaire covering all the questions were provided to all students, and a filled questionnaire was collected from all participating students. All the participant students were requested to fill the questionnaire individually. Response to all the questions from all participant students was entered in Microsoft Excel and analyzed. Active intervention was not attempted in the study, before COVID-19 pandemic traditional classroom teaching was the method of teaching which was changed to online teaching due to restrictions of the COVID-19 pandemic.

Ethical considerations

Consent was taken from all students who participated in the study. Ethical consideration was not required as no active change in teaching modality was there due to the study.

Participants in this study were medical undergraduate students; the questionnaire was sent to a total of 101 students. A total of 75 students participated; female (26.6%) and male (73.3%) were in the age group of 18 and 30 years. We aimed to evaluate students about their perceptions regarding ease or difficulty of online lecture materials, assignments, and online navigation.

Questionnaires were made regarding the students’ concern about understanding of course content, attention scale, convenience, doubts in class, technical issue, distraction during the class, and clarification of the doubts.

Survey reported that 25.3% of students found that online lecture material was satisfactory and easy to understand, while over 40% of students found the lecture material difficult to understand. 42.6% of students found that online assignments were difficult to clear the doubts, while 45.3% found difficulty in attention span during the online classes. Similarly, 64% reported difficulty in the discussion in online classes with the teachers and understanding the course, as shown in Table 1 . The findings further indicate students’ perceptions about the material are viewed as being rigorous even despite the ease of navigation. No comparative analysis was done between the rigor for face-to-face classes and online offerings. However, students perceived a difference between the amounts learned in the two modes even though course content was equivalent.

Questionnaire-based response from students

*Subjective answer mentioned in results. Total number of participants n =75

In academic environments, course organization and presentation are key factors that can either attract or distract students. Students need clarity and relevance in the materials presented to them. 24% of the participants agreed that the online courses were well presented and organized. On the other hand, 64.4% of the participants believe they have learned more in a face-to-face learning environment than in an online setting. Online learning is not always a seamless experience for students. Users encounter many problems including Internet interruption, system upgrade downtime, and instruction and organization to unreliable Internet connection.

Within the last 20 years, the components of learning via computers have challenged the view that the traditional lecture is necessarily the most appropriate means of facilitating learning in a university environment. People found that e-learning has its own advantages on learning outcomes through researches on comparison research about differences between e-learning and traditional classroom.

Over the past decades, most institutions have expanded the list of courses being offered online, and a growing number of students favor online courses over traditional face-to-face courses. This is due in part to the flexibility that online courses provide, the convenience, and a host of other factors. Respondents in this study indicated that offering more online courses would not be that helpful. Some of the students perceived their online experience as being positive despite multiple problems in the online courses, including lack of understanding of the content of materials, limited access, and poor technological infrastructure. In addition, the majority of students found the lecture materials and assignments difficult to understand. These findings suggest that institutions need to address their students’ desire for more flexible, technology-oriented educational platforms and to exert greater efforts to eliminate obstacles that might hinder the smooth utilization of these technologies.

In our study, the responders faced many difficulties. There should be orientation session for teachers and students on how to adapt to online classes and make learning fun and effective through classes before beginning online sessions for students. To ensure discipline is maintained in class, many educational institutions have issued e-classroom etiquette. It includes being properly dressed, being seated at a desk, and no interruptions from parents during the class. Classroom can be split into multiple batches so that it is easier to keep track of students in a session.

A study by Alsaaty et al. [ 3 ] compared and found out online experience as being positive despite multiple problems in the online courses. Thomas et al. [ 4 ] conducted a similar study where he compared students and found out Internet-based course showed higher performance of students on class-based course. Chen et al. [ 5 ] conducted another study where student perceptions in a MBA accounting concluded that the traditional classrooms would continue to offer benefits that cannot fully be obtained in any other manner. However, gaps in process effectiveness will continue to be narrowed as technology becomes friendlier for both instructor and students.

Limitation and recommendation

Since this was a questionnaire-based study, possibility of the participants misinterpreting certain questions cannot be ignored. Response rate was about 75%. Although efforts were made to include open-ended questions, some questions were multiple-choice question based which could have limited the response of participants to few options. Future studies on this subject could use a face-to-face interview approach to get a better response rate and include more subjective and personalized responses of participants. In addition, there was only one time collection of data in this study. Further studies are needed to see if online classes can be an integral part of medical education, once the restrictions due to pandemic ease down.

One of the advantages the online mode offers is its flexibility in timing and delivery. They can even download the content, notes, and assignment. They can easily participate in discussion due to less anxiety and do group discussion and permanent record of feedback. Other advantages for students include not needing to commute. Despite all the advantages offered, there is a general consensus that no technology can replace traditional teaching in real because in this, there will be visual as well verbal discussion. Practical learning through in-hand training, demonstrations, and skill development, which of utmost importance in medical learning, is not possible through online teaching. Teachers might be less conversant and have apathy toward online teaching. It is difficult to keep track of student's attention. Doubt-clearing is hampered as well. Looking at the uncertainty of the current scenario, it is difficult to predict how long online classes will continue. Hence, it is of paramount importance that we assess the effectiveness of online classes and consequently take measures to ensure proper delivery of content to students, especially in a skilled field like medicine.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

Acknowledgment

We are thankful to the Academic Section of All India Institute of Medical Sciences, New Delhi, India, for their support.

A Comparison of Student Learning Outcomes: Online Education vs. Traditional Classroom Instruction

Despite the prevalence of online learning today, it is often viewed as a less favorable option when compared to the traditional, in-person educational experience. Criticisms of online learning come from various sectors, like employer groups, college faculty, and the general public, and generally includes a lack of perceived quality as well as rigor. Additionally, some students report feelings of social isolation in online learning (Protopsaltis & Baum, 2019).

In my experience as an online student as well as an online educator, online learning has been just the opposite. I have been teaching in a fully online master’s degree program for the last three years and have found it to be a rich and rewarding experience for students and faculty alike. As an instructor, I have felt more connected to and engaged with my online students when compared to in-person students. I have also found that students are actively engaged with course content and demonstrate evidence of higher-order thinking through their work. Students report high levels of satisfaction with their experiences in online learning as well as the program overall as indicated in their Student Evaluations of Teaching  (SET) at the end of every course. I believe that intelligent course design, in addition to my engagement in professional development related to teaching and learning online, has greatly influenced my experience.

In an article by Wiley Education Services, authors identified the top six challenges facing US institutions of higher education, and include:

• Declining student enrollment
• Financial difficulties
• Fewer high school graduates
• Decreased state funding
• Lower world rankings
• Declining international student enrollments

Of the strategies that institutions are exploring to remedy these issues, online learning is reported to be a key focus for many universities (“Top Challenges Facing US Higher Education”, n.d.).

Babson Survey Research Group, 2016, [PDF file].

Some of the questions I would like to explore in further research include:

• What factors influence engagement and connection in distance education?
• Are the learning outcomes in online education any different than the outcomes achieved in a traditional classroom setting?
• How do course design and instructor training influence these factors?
• In what ways might educational technology tools enhance the overall experience for students and instructors alike?

In this literature review, I have chosen to focus on a comparison of student learning outcomes in online education versus the traditional classroom setting. My hope is that this research will unlock the answers to some of the additional questions posed above and provide additional direction for future research.

Online Learning Defined

According to Mayadas, Miller, and Sener (2015), online courses are defined by all course activity taking place online with no required in-person sessions or on-campus activity. It is important to note, however, that the Babson Survey Research Group, a prominent organization known for their surveys and research in online learning, defines online learning as a course in which 80-100% occurs online. While this distinction was made in an effort to provide consistency in surveys year over year, most institutions continue to define online learning as learning that occurs 100% online.

Blended or hybrid learning is defined by courses that mix face to face meetings, sessions, or activities with online work. The ratio of online to classroom activity is often determined by the label in which the course is given. For example, a blended classroom course would likely include more time spent in the classroom, with the remaining work occurring outside of the classroom with the assistance of technology. On the other hand, a blended online course would contain a greater percentage of work done online, with some required in-person sessions or meetings (Mayadas, Miller, & Sener, 2015).

A classroom course (also referred to as a traditional course) refers to course activity that is anchored to a regular meeting time.

Enrollment Trends in Online Education

There has been an upward trend in the number of postsecondary students enrolled in online courses in the U.S. since 2002. A report by the Babson Survey Research Group showed that in 2016, more than six million students were enrolled in at least one online course. This number accounted for 31.6% of all college students (Seaman, Allen, & Seaman, 2018). Approximately one in three students are enrolled in online courses with no in-person component. Of these students, 47% take classes in a fully online program. The remaining 53% take some, but not all courses online (Protopsaltis & Baum, 2019).

(Seaman et al., 2016, p. 11)

Perceptions of Online Education

In a 2016 report by the Babson Survey Research Group, surveys of faculty between 2002-2015 showed approval ratings regarding the value and legitimacy of online education ranged from 28-34 percent. While numbers have increased and decreased over the thirteen-year time frame, faculty approval was at 29 percent in 2015, just 1 percent higher than the approval ratings noted in 2002 – indicating that perceptions have remained relatively unchanged over the years (Allen, Seaman, Poulin, & Straut, 2016).

(Allen, I.E., Seaman, J., Poulin, R., Taylor Strout, T., 2016, p. 26)

In a separate survey of chief academic officers, perceptions of online learning appeared to align with that of faculty. In this survey, leaders were asked to rate their perceived quality of learning outcomes in online learning when compared to traditional in-person settings. While the percentage of leaders rating online learning as “inferior” or “somewhat inferior” to traditional face-to-face courses dropped from 43 percent to 23 percent between 2003 to 2012, the number rose again to 29 percent in 2015 (Allen, Seaman, Poulin, & Straut, 2016).

Faculty and academic leaders in higher education are not alone when it comes to perceptions of inferiority when compared to traditional classroom instruction. A 2013 Gallop poll assessing public perceptions showed that respondents rated online education as “worse” in five of the seven categories seen in the table below.

(Saad, L., Busteed, B., and Ogisi, M., 2013, October 15)

In general, Americans believed that online education provides both lower quality and less individualized instruction and less rigorous testing and grading when compared to the traditional classroom setting. In addition, respondents also thought that employers would perceive a degree from an online program less positively when compared to a degree obtained through traditional classroom instruction (Saad, Busteed, & Ogisi, 2013).

Student Perceptions of Online Learning

So what do students have to say about online learning? In  Online College Students 2015: Comprehensive Data on Demands and Preferences,  1500 college students who were either enrolled or planning to enroll in a fully online undergraduate, graduate, or certificate program were surveyed. 78 percent of students believed the academic quality of their online learning experience to be better than or equal to their experiences with traditional classroom learning. Furthermore, 30 percent of online students polled said that they would likely not attend classes face to face if their program were not available online (Clienfelter & Aslanian, 2015). The following video describes some of the common reasons why students choose to attend college online.

How Online Learning Affects the Lives of Students ( Pearson North America, 2018, June 25)

In a 2015 study comparing student perceptions of online learning with face to face learning, researchers found that the majority of students surveyed expressed a preference for traditional face to face classes. A content analysis of the findings, however, brought attention to two key ideas: 1) student opinions of online learning may be based on “old typology of distance education” (Tichavsky, et al, 2015, p.6) as opposed to actual experience, and 2) a student’s inclination to choose one form over another is connected to issues of teaching presence and self-regulated learning (Tichavsky et al, 2015).

Student Learning Outcomes

Given the upward trend in student enrollment in online courses in postsecondary schools and the steady ratings of the low perceived value of online learning by stakeholder groups, it should be no surprise that there is a large body of literature comparing student learning outcomes in online classes to the traditional classroom environment.

While a majority of the studies reviewed found no significant difference in learning outcomes when comparing online to traditional courses (Cavanaugh & Jacquemin, 2015; Kemp & Grieve, 2014; Lyke & Frank 2012; Nichols, Shaffer, & Shockey, 2003; Stack, 2015; Summers, Waigandt, & Whittaker, 2005), there were a few outliers. In a 2019 report by Protopsaltis & Baum, authors confirmed that while learning is often found to be similar between the two mediums, students “with weak academic preparation and those from low-income and underrepresented backgrounds consistently underperform in fully-online environments” (Protopsaltis & Baum, 2019, n.p.). An important consideration, however, is that these findings are primarily based on students enrolled in online courses at the community college level – a demographic with a historically high rate of attrition compared to students attending four-year institutions (Ashby, Sadera, & McNary, 2011). Furthermore, students enrolled in online courses have been shown to have a 10 – 20 percent increase in attrition over their peers who are enrolled in traditional classroom instruction (Angelino, Williams, & Natvig, 2007). Therefore, attrition may be a key contributor to the lack of achievement seen in this subgroup of students enrolled in online education.

In contrast, there were a small number of studies that showed that online students tend to outperform those enrolled in traditional classroom instruction. One study, in particular, found a significant difference in test scores for students enrolled in an online, undergraduate business course. The confounding variable, in this case, was age. Researchers found a significant difference in performance in nontraditional age students over their traditional age counterparts. Authors concluded that older students may elect to take online classes for practical reasons related to outside work schedules, and this may, in turn, contribute to the learning that occurs overall (Slover & Mandernach, 2018).

In a meta-analysis and review of online learning spanning the years 1996 to 2008, authors from the US Department of Education found that students who took all or part of their classes online showed better learning outcomes than those students who took the same courses face-to-face. In these cases, it is important to note that there were many differences noted in the online and face-to-face versions, including the amount of time students spent engaged with course content. The authors concluded that the differences in learning outcomes may be attributed to learning design as opposed to the specific mode of delivery (Means, Toyoma, Murphy, Bakia, Jones, 2009).

Limitations and Opportunities

After examining the research comparing student learning outcomes in online education with the traditional classroom setting, there are many limitations that came to light, creating areas of opportunity for additional research. In many of the studies referenced, it is difficult to determine the pedagogical practices used in course design and delivery. Research shows the importance of student-student and student-teacher interaction in online learning, and the positive impact of these variables on student learning (Bernard, Borokhovski, Schmid, Tamim, & Abrami, 2014). Some researchers note that while many studies comparing online and traditional classroom learning exist, the methodologies and design issues make it challenging to explain the results conclusively (Mollenkopf, Vu, Crow, & Black, 2017). For example, some online courses may be structured in a variety of ways, i.e. self-paced, instructor-led and may be classified as synchronous or asynchronous (Moore, Dickson-Deane, Galyan, 2011)

Another gap in the literature is the failure to use a common language across studies to define the learning environment. This issue is explored extensively in a 2011 study by Moore, Dickson-Deane, and Galyan. Here, the authors examine the differences between e-learning, online learning, and distance learning in the literature, and how the terminology is often used interchangeably despite the variances in characteristics that define each. The authors also discuss the variability in the terms “course” versus “program”. This variability in the literature presents a challenge when attempting to compare one study of online learning to another (Moore, Dickson-Deane, & Galyan, 2011).

Finally, much of the literature in higher education focuses on undergraduate-level classes within the United States. Little research is available on outcomes in graduate-level classes as well as general information on student learning outcomes and perceptions of online learning outside of the U.S.

As we look to the future, there are additional questions to explore in the area of online learning. Overall, this research led to questions related to learning design when comparing the two modalities in higher education. Further research is needed to investigate the instructional strategies used to enhance student learning, especially in students with weaker academic preparation or from underrepresented backgrounds. Given the integral role that online learning is expected to play in the future of higher education in the United States, it may be even more critical to move beyond comparisons of online versus face to face. Instead, choosing to focus on sound pedagogical quality with consideration for the mode of delivery as a means for promoting positive learning outcomes.

Allen, I.E., Seaman, J., Poulin, R., & Straut, T. (2016). Online Report Card: Tracking Online Education in the United States [PDF file]. Babson Survey Research Group.   http://onlinelearningsurvey.com/reports/onlinereportcard.pdf

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• Published: 09 January 2024

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

• Bandar N. Alarifi 1 &
• Steve Song 2  

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

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

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Introduction.

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

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

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

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

Motivation for the study

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

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

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

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

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

Academic achievement: in-person vs online learning

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

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

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

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

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

Theoretical framework

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

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

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

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

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

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

Course description

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

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

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

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

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

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

Structure and activities

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

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

Participants

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

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

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

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

Study instrument

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

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

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

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

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

Limitations

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Data availability

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

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

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

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

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

Introduction

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

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

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

Research questions

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

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

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

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

Literature review

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

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

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

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

Instructional support

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

• Teaching presence

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

Basic online modality

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

Social presence

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

Online social comfort

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

• Cognitive presence

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

Interactive online modality

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

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

Research methods

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

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

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

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

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

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

Measures and procedure

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

Exploratory factor constructs

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

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

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

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

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

Regression results

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

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

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

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

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

Discussion and study limitations

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

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

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

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

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

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

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

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

Availability of data and materials

We will make the data available.

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

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Partisan divides over K-12 education in 8 charts

K-12 education is shaping up to be a key issue in the 2024 election cycle. Several prominent Republican leaders, including GOP presidential candidates, have sought to limit discussion of gender identity and race in schools , while the Biden administration has called for expanded protections for transgender students . The coronavirus pandemic also brought out partisan divides on many issues related to K-12 schools .

Today, the public is sharply divided along partisan lines on topics ranging from what should be taught in schools to how much influence parents should have over the curriculum. Here are eight charts that highlight partisan differences over K-12 education, based on recent surveys by Pew Research Center and external data.

Pew Research Center conducted this analysis to provide a snapshot of partisan divides in K-12 education in the run-up to the 2024 election. The analysis is based on data from various Center surveys and analyses conducted from 2021 to 2023, as well as survey data from Education Next, a research journal about education policy. Links to the methodology and questions for each survey or analysis can be found in the text of this analysis.

Most Democrats say K-12 schools are having a positive effect on the country , but a majority of Republicans say schools are having a negative effect, according to a Pew Research Center survey from October 2022. About seven-in-ten Democrats and Democratic-leaning independents (72%) said K-12 public schools were having a positive effect on the way things were going in the United States. About six-in-ten Republicans and GOP leaners (61%) said K-12 schools were having a negative effect.

About six-in-ten Democrats (62%) have a favorable opinion of the U.S. Department of Education , while a similar share of Republicans (65%) see it negatively, according to a March 2023 survey by the Center. Democrats and Republicans were more divided over the Department of Education than most of the other 15 federal departments and agencies the Center asked about.

In May 2023, after the survey was conducted, Republican lawmakers scrutinized the Department of Education’s priorities during a House Committee on Education and the Workforce hearing. The lawmakers pressed U.S. Secretary of Education Miguel Cardona on topics including transgender students’ participation in sports and how race-related concepts are taught in schools, while Democratic lawmakers focused on school shootings.

Partisan opinions of K-12 principals have become more divided. In a December 2021 Center survey, about three-quarters of Democrats (76%) expressed a great deal or fair amount of confidence in K-12 principals to act in the best interests of the public. A much smaller share of Republicans (52%) said the same. And nearly half of Republicans (47%) had not too much or no confidence at all in principals, compared with about a quarter of Democrats (24%).

This divide grew between April 2020 and December 2021. While confidence in K-12 principals declined significantly among people in both parties during that span, it fell by 27 percentage points among Republicans, compared with an 11-point decline among Democrats.

Democrats are much more likely than Republicans to say teachers’ unions are having a positive effect on schools. In a May 2022 survey by Education Next , 60% of Democrats said this, compared with 22% of Republicans. Meanwhile, 53% of Republicans and 17% of Democrats said that teachers’ unions were having a negative effect on schools. (In this survey, too, Democrats and Republicans include independents who lean toward each party.)

The 38-point difference between Democrats and Republicans on this question was the widest since Education Next first asked it in 2013. However, the gap has exceeded 30 points in four of the last five years for which data is available.

Republican and Democratic parents differ over how much influence they think governments, school boards and others should have on what K-12 schools teach. About half of Republican parents of K-12 students (52%) said in a fall 2022 Center survey that the federal government has too much influence on what their local public schools are teaching, compared with two-in-ten Democratic parents. Republican K-12 parents were also significantly more likely than their Democratic counterparts to say their state government (41% vs. 28%) and their local school board (30% vs. 17%) have too much influence.

On the other hand, more than four-in-ten Republican parents (44%) said parents themselves don’t have enough influence on what their local K-12 schools teach, compared with roughly a quarter of Democratic parents (23%). A larger share of Democratic parents – about a third (35%) – said teachers don’t have enough influence on what their local schools teach, compared with a quarter of Republican parents who held this view.

Republican and Democratic parents don’t agree on what their children should learn in school about certain topics. Take slavery, for example: While about nine-in-ten parents of K-12 students overall agreed in the fall 2022 survey that their children should learn about it in school, they differed by party over the specifics. About two-thirds of Republican K-12 parents said they would prefer that their children learn that slavery is part of American history but does not affect the position of Black people in American society today. On the other hand, 70% of Democratic parents said they would prefer for their children to learn that the legacy of slavery still affects the position of Black people in American society today.

Parents are also divided along partisan lines on the topics of gender identity, sex education and America’s position relative to other countries. Notably, 46% of Republican K-12 parents said their children should not learn about gender identity at all in school, compared with 28% of Democratic parents. Those shares were much larger than the shares of Republican and Democratic parents who said that their children should not learn about the other two topics in school.

Many Republican parents see a place for religion in public schools , whereas a majority of Democratic parents do not. About six-in-ten Republican parents of K-12 students (59%) said in the same survey that public school teachers should be allowed to lead students in Christian prayers, including 29% who said this should be the case even if prayers from other religions are not offered. In contrast, 63% of Democratic parents said that public school teachers should not be allowed to lead students in any type of prayers.

In June 2022, before the Center conducted the survey, the Supreme Court ruled in favor of a football coach at a public high school who had prayed with players at midfield after games. More recently, Texas lawmakers introduced several bills in the 2023 legislative session that would expand the role of religion in K-12 public schools in the state. Those proposals included a bill that would require the Ten Commandments to be displayed in every classroom, a bill that would allow schools to replace guidance counselors with chaplains, and a bill that would allow districts to mandate time during the school day for staff and students to pray and study religious materials.

Mentions of diversity, social-emotional learning and related topics in school mission statements are more common in Democratic areas than in Republican areas. K-12 mission statements from public schools in areas where the majority of residents voted Democratic in the 2020 general election are at least twice as likely as those in Republican-voting areas to include the words “diversity,” “equity” or “inclusion,” according to an April 2023 Pew Research Center analysis .

Also, about a third of mission statements in Democratic-voting areas (34%) use the word “social,” compared with a quarter of those in Republican-voting areas, and a similar gap exists for the word “emotional.” Like diversity, equity and inclusion, social-emotional learning is a contentious issue between Democrats and Republicans, even though most K-12 parents think it’s important for their children’s schools to teach these skills . Supporters argue that social-emotional learning helps address mental health needs and student well-being, but some critics consider it emotional manipulation and want it banned.

In contrast, there are broad similarities in school mission statements outside of these hot-button topics. Similar shares of mission statements in Democratic and Republican areas mention students’ future readiness, parent and community involvement, and providing a safe and healthy educational environment for students.

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Jenn Hatfield is a writer/editor at Pew Research Center

Most Americans think U.S. K-12 STEM education isn’t above average, but test results paint a mixed picture

About 1 in 4 u.s. teachers say their school went into a gun-related lockdown in the last school year, about half of americans say public k-12 education is going in the wrong direction, what public k-12 teachers want americans to know about teaching, what’s it like to be a teacher in america today, most popular.

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Introducing Phi-3: Redefining what’s possible with SLMs

By Misha Bilenko Corporate Vice President, Microsoft GenAI

Posted on April 23, 2024 4 min read

• Tag: Copilot
• Tag: Generative AI

We are excited to introduce Phi-3, a family of open AI models developed by Microsoft. Phi-3 models are the most capable and cost-effective small language models (SLMs) available, outperforming models of the same size and next size up across a variety of language, reasoning, coding, and math benchmarks. This release expands the selection of high-quality models for customers, offering more practical choices as they compose and build generative AI applications.

Starting today, Phi-3-mini , a 3.8B language model is available on Microsoft Azure AI Studio , Hugging Face , and Ollama . 

• Phi-3-mini is available in two context-length variants—4K and 128K tokens. It is the first model in its class to support a context window of up to 128K tokens, with little impact on quality.
• It is instruction-tuned, meaning that it’s trained to follow different types of instructions reflecting how people normally communicate. This ensures the model is ready to use out-of-the-box.
• It is available on Azure AI to take advantage of the deploy-eval-finetune toolchain, and is available on Ollama for developers to run locally on their laptops.
• It has been optimized for ONNX Runtime with support for Windows DirectML along with cross-platform support across graphics processing unit (GPU), CPU, and even mobile hardware.
• It is also available as an NVIDIA NIM microservice with a standard API interface that can be deployed anywhere. And has been optimized for NVIDIA GPUs . 

In the coming weeks, additional models will be added to Phi-3 family to offer customers even more flexibility across the quality-cost curve. Phi-3-small (7B) and Phi-3-medium (14B) will be available in the Azure AI model catalog and other model gardens shortly.   

Microsoft continues to offer the best models across the quality-cost curve and today’s Phi-3 release expands the selection of models with state-of-the-art small models.

Azure AI Studio

Phi-3-mini is now available

Groundbreaking performance at a small size 

Phi-3 models significantly outperform language models of the same and larger sizes on key benchmarks (see benchmark numbers below, higher is better). Phi-3-mini does better than models twice its size, and Phi-3-small and Phi-3-medium outperform much larger models, including GPT-3.5T.  

All reported numbers are produced with the same pipeline to ensure that the numbers are comparable. As a result, these numbers may differ from other published numbers due to slight differences in the evaluation methodology. More details on benchmarks are provided in our technical paper . 

Note: Phi-3 models do not perform as well on factual knowledge benchmarks (such as TriviaQA) as the smaller model size results in less capacity to retain facts.  

Safety-first model design 

Responsible ai principles

Phi-3 models were developed in accordance with the Microsoft Responsible AI Standard , which is a company-wide set of requirements based on the following six principles: accountability, transparency, fairness, reliability and safety, privacy and security, and inclusiveness. Phi-3 models underwent rigorous safety measurement and evaluation, red-teaming, sensitive use review, and adherence to security guidance to help ensure that these models are responsibly developed, tested, and deployed in alignment with Microsoft’s standards and best practices.  

Building on our prior work with Phi models (“ Textbooks Are All You Need ”), Phi-3 models are also trained using high-quality data. They were further improved with extensive safety post-training, including reinforcement learning from human feedback (RLHF), automated testing and evaluations across dozens of harm categories, and manual red-teaming. Our approach to safety training and evaluations are detailed in our technical paper , and we outline recommended uses and limitations in the model cards. See the model card collection .  

Unlocking new capabilities 

Microsoft’s experience shipping copilots and enabling customers to transform their businesses with generative AI using Azure AI has highlighted the growing need for different-size models across the quality-cost curve for different tasks. Small language models, like Phi-3, are especially great for: 

• Resource constrained environments including on-device and offline inference scenarios.
• Latency bound scenarios where fast response times are critical.
• Cost constrained use cases, particularly those with simpler tasks.

For more on small language models, see our Microsoft Source Blog .

Thanks to their smaller size, Phi-3 models can be used in compute-limited inference environments. Phi-3-mini, in particular, can be used on-device, especially when further optimized with ONNX Runtime for cross-platform availability. The smaller size of Phi-3 models also makes fine-tuning or customization easier and more affordable. In addition, their lower computational needs make them a lower cost option with much better latency. The longer context window enables taking in and reasoning over large text content—documents, web pages, code, and more. Phi-3-mini demonstrates strong reasoning and logic capabilities, making it a good candidate for analytical tasks. 

Customers are already building solutions with Phi-3. One example where Phi-3 is already demonstrating value is in agriculture, where internet might not be readily accessible. Powerful small models like Phi-3 along with Microsoft copilot templates are available to farmers at the point of need and provide the additional benefit of running at reduced cost, making AI technologies even more accessible.  

ITC, a leading business conglomerate based in India, is leveraging Phi-3 as part of their continued collaboration with Microsoft on the copilot for Krishi Mitra, a farmer-facing app that reaches over a million farmers.

“ Our goal with the Krishi Mitra copilot is to improve efficiency while maintaining the accuracy of a large language model. We are excited to partner with Microsoft on using fine-tuned versions of Phi-3 to meet both our goals—efficiency and accuracy! ”    Saif Naik, Head of Technology, ITCMAARS

Originating in Microsoft Research, Phi models have been broadly used, with Phi-2 downloaded over 2 million times. The Phi series of models have achieved remarkable performance with strategic data curation and innovative scaling. Starting with Phi-1, a model used for Python coding, to Phi-1.5, enhancing reasoning and understanding, and then to Phi-2, a 2.7 billion-parameter model outperforming those up to 25 times its size in language comprehension. 1 Each iteration has leveraged high-quality training data and knowledge transfer techniques to challenge conventional scaling laws. 

Get started today 

To experience Phi-3 for yourself, start with playing with the model on Azure AI Playground . You can also find the model on the Hugging Chat playground . Start building with and customizing Phi-3 for your scenarios using the  Azure AI Studio . Join us to learn more about Phi-3 during a special  live stream of the AI Show.  

1 Microsoft Research Blog, Phi-2: The surprising power of small language models, December 12, 2023 .

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