This study aimed to identify factors that impact graduate health students’ preference for synchronous or asynchronous online lecture participation. Constructive factors were proposed and then measured for each participant via the Motivated Strategies for Learning Questionnaire scales. It was hypothesized that students ranking lower in the domains of self-efficacy (confidence in mastering a task) and time management (ability to manage time) while higher in the domain of peer learning (communicating with peers to improve understanding) would favor synchronous lecture participation. Conversely, students ranking higher in self-efficacy and time management and lower in peer learning would favor asynchronous lecture participation. In a survey, participants were asked to indicate a preference for synchronous or asynchronous online lecture participation and indicate agreement with various item statements regarding perceptions of online learning.
Multiple logistic regression was utilized to determine which factors were associated with student preference for an online lecture. Peer learning significantly impacted students’ decisions, while time management and self-efficacy were not deemed influential. Other factors, including student perception of convenience, ease of concentrating during lectures, and the role of online lectures within graduate health programs, exhibited significant differences between synchronous and asynchronous groups. Overall, students within the synchronous group valued peer learning more, reported less difficulty concentrating during lectures, and expressed less desire for online lectures to be a part of their graduate health program. Both synchronous and asynchronous groups believed that decisions regarding online lecture participation are made with respect to how they best learn.
Introduction
A trend toward higher levels of online learning in graduate health programs has been accelerated with the onset of the COVID-19 pandemic. Greater student autonomy and flexibility have been achieved with a partial or complete shift of coursework online. While a return to traditional learning has occurred for most graduate health programs, online instruction remains a viable and useful option (Afshan & Ahmed, 2020). Programs that previously mandated student physical attendance for a class have explore options for synchronous and asynchronous online participation. When given the option to participate either synchronously or asynchronously in an online lecture, a gap in the literature exists regarding factors that contribute to student preference. Presumably, student decisions derive from perceived differences between the two options. These differences may stem from factors thought to impact the quality of the learning experience or, alternatively, factors such as convenience.
Assistant Professor, Department of Physical Therapy, University of South Dakota, 414 East Clark, Vermillion, SD 57069.
Assistant Professor, Department of Physical Therapy, University of South Dakota, 414 East Clark, Vermillion, SD 57069.
Professor and Department Chair, Department of Physical Therapy, University of South Dakota, 414 East Clark, Vermillion, SD 57069.
Professor and Department Chair, Department of Physical Therapy, University of South Dakota, 414 East Clark, Vermillion, SD 57069.
Professor, Director of Research, Department of Occupational Therapy, University of South Dakota, 414 East Clark, Vermillion, SD 57069.
Professor, Director of Research, Department of Occupational Therapy, University of South Dakota, 414 East Clark, Vermillion, SD 57069.
Assistant Professor, Doctor of Physical Therapy Program, Tufts University School of Medicine, 136 Harrison Ave., Boston, MA 02111.
Assistant Professor, Doctor of Physical Therapy Program, Tufts University School of Medicine, 136 Harrison Ave., Boston, MA 02111.
Factors contributing to student preference for online or traditional learning may be similar to factors impacting student preference for synchronous or asynchronous online participation. A review of the current literature demonstrates consistency in identifying factors contributing to student learning preference when deciding between online and traditional learning. Socioeconomic factors, convenience, flexibility, and compatibility with a fulltime work schedule impact preference for online or traditional learning (Harris & Martin, 2012). Less is known about factors contributing to student learning preference when deciding between synchronous and asynchronous online lectures. Concerning the available literature and recognizing the differences between synchronous and asynchronous online participation, this study investigates how the perception of self-efficacy, time management, and peer learning impact students’ preference for synchronous or asynchronous online participation in online lectures.
Background and Purpose
The impact of COVID-19 on global education has included a shift of traditional programs toward online learning that will likely persist following the resolution of the pandemic (Afshan & Ahmed, 2020). While traditionally conducted asynchronously, advancements in technology, allowing synchronous interaction through platforms such as Zoom and Google Meet, have facilitated an evolution in online learning (Watts, 2016). In stark contrast to more rigid expectations for face-to-face participation, students in graduate health programs are now afforded synchronous or asynchronous online participation options. It is unclear if the method of learning flexibility improves or hinders outcomes for this unique population. As students selecting traditional programs consented to instruction in more structured and inflexible methods, this student population may not require the flexibility and autonomy afforded by online learning. As a result, when learning online, recommendations have been proposed to mandate synchronous participation for traditional students to avoid potential pitfalls of decreased student engagement with asynchronous participation (Guo, 2020). Clarification of the factors influencing student decision-making would offer insight into the legitimacy of student preference. Are students selecting a method of participation based on self-awareness of optimal learning styles, or are decisions based on convenience? Assessing the underlying factors contributing to online lecture preference is best understood by exploring the basic differences between online and traditional learning and synchronous and asynchronous online lecture.
Online Learning Versus Traditional Learning
Online learning, originating as distance learning (J. L. Moore et al., 2011), was initially synonymous with anytime-any-where e-learning (Watts, 2016). Online learning offers flexibility and convenience that is less readily available with traditional learning. However, the freedom of learning afforded by online learning poses potential threats to student perception of engagement when online courses fail to promote interaction typical of traditional learning environments (Aziz et al., 2020). Realizing these potential threats has resulted in an expansion of online learning to include synchronous interaction.
Synchronous Online Learning Versus Asynchronous Online Learning
Online learning occurs via synchronous or asynchronous participation. Asynchronous learning allows students flexibility in content review and communication through discussion boards or email (Hrastinski, 2008). The anytime-anywhere quality of online learning is preserved with the asynchronous format, however, at the potential cost of student engagement and interaction (Akyol & Garrison, 2008; M. Moore, 1997). In contrast, synchronous participation implies participation in realtime, allowing instantaneous feedback between student and instructor. Synchronous learning, which requires attendance at a specific date and time, may contradict “the promise of ‘anytime, anywhere’ learning that online courses have traditionally promoted” (Skylar, 2009, p. 71). Alternatively, asynchronous participation encompasses the flexibility and convenience previously expected of online learning, while synchronous participation offers structure and interaction more synonymous with traditional learning.
Determination of Factors Impacting Student Preference
Factors impacting student preference for online or traditional learning are apparent; however, there is a gap in the literature about factors affecting preference for synchronous or asynchronous lecture format. Students selecting online learning, usually comprising an older demographic, typically favor the convenience and flexibility often available with online programming, allowing for completing other life demands while attending classes (Harris & Martin, 2012). Presence of a full-time job (Chow, 2013; Ilgaz & Gulbahar, 2017; Liu, 2011), time management (Ilgaz & Gulbahar, 2017; Liu, 2011; Smith, 2005), flexibility (Ilgaz & Gulbahar, 2017; Liu, 2011), and student comfort with technology (Liu, 2011; Smith, 2005) are common factors contributing to a preference for online learning over traditional learning. Among traditional students now engaging in online learning, factors impacting student preference for synchronous or asynchronous participation are unclear but can be surmised.
Self-Efficacy
As introduced by psychologist Albert Bandura, self-efficacy describes individuals’ beliefs in their capabilities to influence events impacting their lives (Bandura, 1986). Pintrich and colleagues (1991) describe self-efficacy as a “self-appraisal of one’s ability to master a task. Self-efficacy includes judgments about one’s ability to accomplish a task as well as one’s confidence in one’s skills to perform that task” (p. 14). In online (Bradley et al., 2017; Joo et al., 2013) and blended (Ying, 2020) learning, student perception of self-efficacy has been positively associated with course outcomes. The flexibility associated with online learning necessitates increased student responsibility for learning. Students require self-regulation to schedule when learning occurs and selfefficacy in personal beliefs that the material can be understood using the chosen delivery method (Wang et al., 2013). Alkiş and Temizel (2018) found that students ranking higher in self-efficacy, as assessed on the Motivated Strategies for Learning Questionnaire, were significantly more likely to engage in online content on a learning management system. The authors concluded that online students “do not attend face-to-face lectures and activities and therefore they usually study by themselves, which requires higher self-efficacy and discipline” (Alkiş & Temizel, 2018, p. 43). Individuals electing online learning, or any learning requiring higher levels of selfdirection (i.e., asynchronous participation), should exhibit confidence in the material and method. Therefore, self-efficacy is anticipated to impact student preference for one method of online participation over the other.
Time Management
Pintrich and colleagues (1991) asserted that: “Time management involves scheduling, planning, and managing one’s study time. This includes not only setting aside blocks of time to study, but the effective use of that study time, and setting realistic goals” (p. 25). Preserving the anytime-anywhere learning model previously synonymous with online learning (Ilgaz & Gulbahar, 2017; Liu, 2011; Smith, 2005), asynchronous participation allows students flexibility in scheduling when learning occurs. Synchronous online participation offers less flexibility and convenience than asynchronous participation; however, it also provides structure and timelines that may appeal to specific students. While not readily explored in the available body of literature, students’ perceptions of the effectiveness of time management should factor into decision-making when deciding between synchronous and asynchronous lecture participation. Therefore, time management is proposed as a factor influencing online learning preference.
Peer Learning
Defined by Pintrich and colleagues (1991), peer learning describes the educational affinity to collaborate with peers to “clarify course material and reach insights one may not have attained on one’s own” (p. 28). Garrison et al. (2000) recognized the benefit of establishing a community of inquiry within online learning, noting that student satisfaction, engagement, and outcomes improved with enhanced social, cognitive, and teaching presence. Peer learning is proposed as a factor in decision-making when choosing between synchronous and asynchronous online lecture participation. Past research has demonstrated that students enrolled in traditional learning desire higher levels of social interaction than students enrolled in online learning (Drouin & Vartanian, 2010). Extending this concept to synchronous and asynchronous participation, student desire for peer interaction, allowing real-time student-instructor and student-student communication, is proposed to impact preference for one method of participation over the other.
Assessing the Validity of the Proposed Factors
The factors of self-efficacy, time management, and peer learning have been proposed as significant influences on student preference for online learning over traditional learning and factors that differentiate synchronous and asynchronous participation. Interaction of these factors has been proposed as a conceptual framework that impacts graduate health students’ preference for online methods of participation if decisions are made in consideration of optimal learning preferences. If student preference for one method occurs based on perceived compatibility with individual learning preferences or motivations, it is reasonable to expect an interaction of these variables on student preference. Therefore, it is hypothesized that students ranking lower in the domains of self-efficacy (confidence in mastering a task) and time management (ability to manage time) while higher in the domain of peer learning (communicating with peers to improve understanding) will favor synchronous lecture participation. Conversely, students ranking higher in self-efficacy and time management and lower in peer learning will favor asynchronous lecture participation (Figure 1). If findings oppose this proposal, either the conceptual framework is inaccurate, or an extraneous variable influences students’ stated preference for online participation. If the latter is suspected, the effectiveness by which students self-select online lecture delivery methods should be called into question. If no correlation is found, further research is needed to determine other factors impacting students’ decisions.
Convenience
It is hypothesized that the perception of convenience plays a significant role in student decision-making. This study gauges students’ self-reported impact of convenience on decision-making in addition to the previously proposed factors. Although perceived as a benefit for students, the factor of convenience resides in a separate category of influence. Indeed, awareness and decision-making based on the aforementioned factors (self-efficacy, time management, and peer learning) are deemed constructive to learning, while convenience is convenient. By recognizing factors contributing to student decision-making, instructors would better understand their students’ motivations and/or learning preferences. Should multiple options of online lecture participation be allowed for constructive learning preferences, or are more options simply catering to student preferences for convenience? As such, this study aims to identify factors contributing to graduate health students’ online learning preferences when choosing between synchronous and asynchronous lectures.
Methods
Design
This research study was conducted using a cross-sectional survey design to investigate the impact of student perception of self-efficacy, time management, and peer learning on preference for synchronous or asynchronous online learning. This design was chosen to acquire quantitative data from a sample of convenience that can be readily analyzed to compare two groups: synchronous and asynchronous preference. This study (IRB-21-139) was approved by the Institutional Review Board of the University of South Dakota, which is fully accredited by the Association for the Accreditation of Human Research Protection Programs.
Participants
Participants in this study were first-and second-year physical and occupational therapy students at the University of South Dakota. The research team approached participants during scheduled classes. Participation was voluntary; however, students were incentivized to complete the study with gift cards. Inclusion criteria included (a) enrollment in the first or second year of physical or occupational therapy school at the University of South Dakota, and (b) prior experience as a student participating in synchronous and asynchronous online lectures. Exclusion criteria included (a) unwillingness to participate in the study; and (b) unavailability on the designated date of the survey.
Instruments
The survey (Appendix A) consists of three scales from the Motivated Strategies for Learning Questionnaire (MSLQ; P. Pintrich et al., 1991). With almost 5,000 citations in Google Scholar, The MSLQ offers educators a self-report assessment of students’ motivational orientations and preferences for different learning strategies. The MSLQ has proven valid and reliable for college students (Davenport, 2003; P. R. Pintrich & Smith, 1993). While the widely used (1991) version of the MSLQ consists of 81 items across 15 scales, the scales are available for individual or collective use depending on the instructor’s needs (P. Pintrich et al., 1991). Included scales and corresponding reliabilities via Cronbach’s alpha measures (P. Pintrich et al., 1991) are Self-Efficacy for Learning and Performance (.93), Time and Study Environment Management (.76), and Peer Learning (.76), indicating internal consistency ranging from acceptable to good for surveys utilizing Likert-style scales (Gliem & Gliem, 2003). Likert-style item statements received minor modifications to reflect student perception of motivation and learning strategies across all courses rather than a single course. For instance, item number 29, “I expect to do well in this class,” was changed to, “I expect to do well in my classes.”
In addition to scales from the MSLQ, 5-item statements generated by the authors of this study were included in the survey, as well as the question: Do you prefer to participate in online lectures synchronously or asynchronously?
Procedures
As approved by the institutional review board, consent was achieved with an ecover letter placed at the beginning of the survey. The consent form included information regarding the purpose of the study, how gathered data would be used and stored, information regarding privacy and protection of participant responses, contact information for the principal investigator, and a definition of terms regarding synchronous and asynchronous online lectures. All participants completed the survey in one sitting. Only the research team had access to the database.
Data Analysis
Data were analyzed using the Statistical Package for the Social Sciences (SPSS, Version 27.0). Independent variables included self-efficacy, time management, and peer learning. The dependent variable was the choice of online learning mode (synchronous or asynchronous). Multivariate binary logistic regression was used to determine the impact a combination of previously selected variables has on student selection when deciding between synchronous or asynchronous online lecture participation. Additionally, independent t tests were utilized to compare between-group differences in synchronous and asynchronous mean scores across the categories of self-efficacy, time management, peer learning, and questions about the perception of online learning.
Results
In total, 121 first-or second-year physical or occupational therapy students were asked to participate in the survey. A total of 115 individuals agreed to participate, and 114 completed the entire survey. The mean age for participants was 22.9 years.
Online Lecture Preference
Online lecture preference was 46.5% (n = 53) synchronous and 53.5% (n = 61) asynchronous. Concerning factors known to impact preference for hybrid over traditional programs, only two participants reported having a full-time job. Part-time job status was reported by 39 students (34.2% of the total sample size). Assessed between groups, Chi-square analysis assessed between groups demonstrated no significant interaction (p = .104) between job status and lecture preference.
In comparing synchronous and asynchronous groups on the MSLQ scales, only the mean scale scores for peer learning significantly differed between groups (p = .018). Participants who preferred synchronous online lecture participation reported a higher self-report desire for peer learning. Mean scores for self-efficacy and time management were higher for the synchronous group but not statistically significant.
The multiple logistic regression analysis (Table 2), utilizing the three predictors (self-efficacy, time management, and peer learning), was statistically significant (p = .027). However, only peer learning (p = .023) was assessed individually and demonstrated a significant difference between groups. Additionally, self-efficacy appears to add no predictive value to the overall model. Results from the model have been preserved, nonetheless, to assess the accuracy of the initial hypothesis. The model correctly classified 68.9% of participants preferring asynchronous lectures and 52.8% of participants preferring synchronous, for 61.4% overall accuracy. Assessed via Nagelkerke R Square, the model accounted for 9.8% of the variance between lecture preferences.
Differences in MSLQ Scale Scores by Lecture Preference
| Total (n = 114) | Synchronous (n = 53) | Asynchronous (n = 61) | p | Cohen’s d | |
|---|---|---|---|---|---|
| Self-efficacy | 45.17 (5.98) | 45.70 (5.32) | 44.70 (6.51) | .379 | .166 |
| Time management | 45.53 (5.91) | 46.53 (5.34) | 44.66 (6.27) | .091 | .320 |
| Peer learning | 14.11 (3.84) | 15.02 (3.65) | 13.33 (3.85) | .018 | .450 |
Note: This table depicts the results of independent t tests between synchronous and asynchronous groups.
Logistic Regression for Synchronous Preference by MSLQ Scale Scores
| Predictor | β | SE | Wald | df | P | OR | 95% CI |
|---|---|---|---|---|---|---|---|
| Self-efficacy | –.001 | .035 | .000 | 1 | .985 | .999 | .933–1.070 |
| Time management | .059 | .037 | 2.614 | 1 | .106 | 1.061 | .987–1.140 |
| Peer | .124 | .055 | 5.193 | 1 | .023 | 1.133 | 1.018–1.261 |
| Constant | –4.585 | 2.077 | 4.872 | 1 | .027 | .010 |
Note: β = beta coefficient, SE = standard error, Wald = Wald Chi-Square, df = degrees of freedom; p = significance of coefficient, OR = odds ratio as ExpB, CI = confidence interval for OR.
Significant differences between synchronous and asynchronous groups were demonstrated on four of the five additional questions about the perception of online learning and individual learning traits (Table 3). Participants were asked to rate their agreement with statements on a 7-point scale, with 1 serving as “not at all true of me, “ 4 as “neutral,” and 7 as “very true of me.” Asynchronous participants reported significantly higher agreement on questions about the role convenience plays in decision-making, the presence of other life demands making scheduled sessions challenging to attend, trouble concentrating during lectures, and belief that online learning should be a part of their graduate program. Notably, both groups reported relative agreement with the statement, “I choose my method of lecture participation based on how I believe I will best learn,” with a lack of significant difference noted between groups.
Questions Pertaining to Online Lecture by Method of Participation
| Median Synchronous (n = 53) | Median Asynchronous (n = 61) | U | Sig | r | |
|---|---|---|---|---|---|
| I choose my method of online lecture participation based on my perception of convenience. | 4 (1–6) | 5 (2–7) | 898.00 | < .001 | .394 |
| I choose my method of online lecture participation based on how I believe I will best learn. | 5 (2–7) | 5 (2–7) | 1,362.00 | .138 | .139 |
| I have other life demands that make scheduled sessions difficult to attend. | 3 (1–6) | 4 (1–7) | 1,245.50 | .032 | .201 |
| I have trouble concentrating during lecture. | 4 (1–6) | 4 (2–7) | 1,147.00 | .007 | .255 |
| I believe online lectures should be a part of my graduate program. | 3 (1–6) | 4 (1–7) | 880.50 | < .001 | .399 |
Note: This table depicts Median scores (range) and the results of Mann-Whitney U tests between synchronous and asynchronous groups. Effect size is calculated via Pearson r.
Discussion
Preference for the synchronous or asynchronous lecture was nearly equally split, demonstrating the diversity of thought within a classroom, even for students primarily thought of as a homogenous group (traditional students, Midwestern university, graduate program, health discipline). While decision-making for synchronous or asynchronous online lecture participation appeared to be impacted by the individual desire for peer interaction, time management and self-efficacy variables were not identified as contributing factors. In constructing the model, these factors were presented as idealistic, noting the benefits student self-appraisal of time management skills and confidence in independent mastery of the material might have on decision-making when deciding between the two formats. It appears idealism is not the most appropriate framework to surmise the factors impacting student decisionmaking.
The lack of significant findings necessitates further identification of covariates impacting students’ decisions for online lecture participation. Some of these variables were proposed during survey development, resulting in the inclusion of additional questions about convenience, perceived business, concentration during lectures, and affinity for online lectures. Notably, significant differences between asynchronous and synchronous preference groups were revealed in each of these categories. Students preferring asynchronous lectures acknowledged the role convenience plays in decision-making. Despite acknowledging convenience as a factor, group differences in response to the statement: “I choose my method of online lecture participation based on how I believe I will best learn,” were insignificant. As such, students choosing asynchronous delivery of online lectures largely perceive no trade-off between convenience and effectiveness of learning. This leads to another research question: does more convenient learning lead to more effective learning? Perhaps the effectiveness of learning is impacted by students’ ability to concentrate. As reflected in participant responses, students preferring asynchronous online lectures reported significantly more difficulty focusing during lectures. Indeed, serving as one of the multiple benefits of asynchronous delivery, students can pause or replay lectures, allowing for the anytime-anywhere style of delivery.
There appear to be significant differences in perception of online lectures between groups of students who prefer synchronous and asynchronous online lectures. Students favoring synchronous format reported negative perceptions toward online lectures, demonstrating a median rating of 3/7 on the item statement “I believe online lectures should be a part of my graduate program.” Although significantly higher, students favoring asynchronous format reported a neutral perception of online lectures, demonstrating a median rating of 4/7 on the same item statement. These results should not be overgeneralized, as this population selected traditional programming for their graduate health education. The students within this study generally lacked full-time jobs and exhibited mean age consistent with traditional students. Nonetheless, this information may be helpful to other traditional graduate health programs engaging in higher levels of online learning. When offering lectures online, the students showing up for synchronous lectures may choose the method that most closely mimics the learning atmosphere typical of face-to-face learning.
Conclusion
The desire for peer interaction during learning impacted the preference for synchronous or asynchronous online lectures for these students enrolled in traditional graduate health programs. On the other hand, self-appraisal of time management skills and self-efficacy did not impact online lecture preference. This finding was in opposition to the idealistic hypothesis that students self-appraise these three qualities and base decisions on them. As a result, other factors impact students’ decisions when deciding between synchronous and asynchronous online lectures. Possible influences identified within this study include student perception of lecture convenience, trouble concentrating during lecture, and overall opinions regarding the role of online lectures in graduate health programs. Students preferring synchronous online lectures generally did not believe online lectures should be a part of their graduate program. Notably, this study population differed from the population of students described within the literature who select online or hybrid education. Regardless of preference, students stated that they made decisions regarding online lecture participation based on how they believed they would best learn.
Acknowledgments
There are no funding sources to report. The authors (Ladwig, Berg-Poppe, Ikiugu, and Ness) have no conflicts of interest to declare.
References
Appendix
Appendix: Survey
Demographic
Name: (blank text box)
Age: (blank text box)
Within the last year, approximately how many hours have you spent watching synchronous online lectures each week? (0 hours, 0.1–2.9, 3.0–5.9, 6.0–7.9, > 8 hours)
Within the last year, approximately how many hours have you spent watching asynchronous online lectures each week? (0 hours, 0.1–2.9, 3.0–5.9, 6.0–7.9, > 8 hours)
Do you have a full-time job? Yes/No
Did you participate in online lectures prior to the COVID-19 pandemic? Yes/ No
Assessed on the following scale:
Self-Efficacy For Learning and Performance
I believe I will receive an excellent grade.
I’m certain I can understand the most difficult material presented in the readings.
I’m confident I can understand the basic concepts taught.
I’m confident I can understand the most complex material presented by instructors.
I’m confident I can do an excellent job on assignments and tests.
I expect to do well.
I’m certain I can master the skills being taught.
Considering the difficulty of the courses, the teachers, and my skills, I think I will do well.
Time and Study Environmental Management
9. I usually study in a place where I can concentrate on my course work.
10. I make good use of my study time.
11. I find it hard to stick to a study schedule. (REVERSED)
12. I have a regular place set aside for studying.
13. I make sure I keep up with the weekly readings and assignments.
14. I attend class regularly.
15. I often find that I don’t spend very much time on courses because of other activities. (REVERSED)
16. I rarely find time to review my notes or readings before an exam. (REVERSED)
Peer Learning
17. When studying, I often try to explain the material to a classmate or a friend.
18. I try to work with other students to complete course assignments.
19. When studying, I often set aside time to discuss course material with a group of students from the class.
Additional Questions
20. I choose my method of online lecture participation based on my perception of convenience.
21. I choose my method of online lecture participation based on how I believe I will best learn.
22. I have other life demands that make scheduled sessions difficult to attend.
23. I have trouble concentrating during lecture.
24. I believe online learning should be a part of my graduate program.
Multiple Choice
25. Do you prefer to participate in online lectures synchronously or asynchronously? Options: synchronously, asynchronously.
