Volume 3, Issue 3: Autumn 2022. DOI: 10.1037/tmb0000085
Videoconference software such as Zoom has facilitated the recent pandemic- fueled explosion in online learning. In two field studies, we explored how students having their cameras on versus off affected their engagement during and their fatigue after a large online lecture-based class. In a longitudinal study (Study 1), we observed N = 65 students enrolled in two online university courses for several weeks (Nobs = 319); we found that when students had their cameras on, they were more engaged—but not more fatigued—than when they had their cameras off. In a subsequent affective forecasting study (Study 2), conducted a year later when classes were back in-person, we asked 81 students across the same two university courses to predict how they would feel if they had their cameras on versus off during a particular class session. Consistent with students’ actual experiences in Study 1, students in Study 2 expected to feel more engaged when their cameras were on than when they were off. Contrary to our findings in Study 1, students in Study 2 predicted that having their cameras on would increase their fatigue. In sum, our findings suggest that, at least in the context of large online lectures, students may overestimate the cost of having their cameras on, and that student camera usage can increase engagement without increasing fatigue.
Keywords: online class, engagement, Zoom fatigue, videoconferencing, COVID-19
Supplemental materials: https://doi.org/10.1037/tmb0000085.supp
Funding: No external funding was used for this project.
Disclosure: The authors have no conflict of interest to disclose.
Data Availability: The data and materials for both studies can be accessed through the Open Science Framework (OSF): https://doi.org/10.17605/OSF.IO/M63SG.
Correspondence concerning this article should be addressed to Kostadin Kushlev, Department of Psychology, Georgetown University, 37th and O Streets, N.W., Washington, DC 20057, United States [email protected]
At the inception of the COVID-19 pandemic, people quickly found themselves limited almost exclusively to virtual interactions in myriad contexts, including education. Online learning had already been on the rise (Seaman et al., 2018), but the pandemic catalyzed this trend, forcing many universities suddenly to transition either to a predominantly or fully online format. Only a few months into the pandemic, in June of 2020, more than 700 universities were holding classes online using the video communication platform Zoom (2020). Even as quarantine restrictions are being lifted, there is a general consensus that virtual learning platforms will remain an indispensable educational tool (George et al., 2021; Lockee, 2021; Martin, 2021; McMurtie, 2021; Pokhrel & Chhetri, 2021).
At the same time, experts, educators, parents, and students agree that online classes are less engaging than in-person ones. In a recent poll of 350 college students, 80% of respondents found it harder to focus in newly online classes (Peper et al., 2021). Given these concerns, the importance of exploring ways to improve virtual learning has never been greater. Modern videoconferencing technology is remarkably flexible, allowing users to have their camera on or off, hide their own video feed, touch up their appearance, and even artificially manipulate their eye gaze so that it appears that they are looking at the camera (Turner, 2021). In the present research, we zeroed in on one, easily manipulatable aspect of online learning—students’ camera usage—and explored how it affects engagement and fatigue.
As the COVID-19 pandemic forced people to learn and work remotely, the term “Zoom fatigue” quickly entered popular parlance; it describes feeling exhausted as a result of extensive video conferencing (e.g., Degges-White, 2020; Jiang, 2020; Morris, 2020). But what causes Zoom fatigue? Recent evidence suggests that Zoom fatigue stems, in part, from camera usage (Fauville et al., 2021; Shockley et al., 2021). For example, in a 4-week field experiment, 101 health care company workers reported that they felt more fatigued at the end of the workday when assigned to have their cameras on during meetings than when assigned to have them off (Shockley et al., 2021). The researchers interpreted these findings using self-presentation theory (Baumeister, 1982; Goffman, 1959), which posits that people are motivated to be viewed favorably by others. The self-regulation required to present oneself favorably involves an inherently fatiguing cognitive demand (Klotz et al., 2018; Vohs et al., 2005). Similarly, Bailenson (2021) theorized that the perceived size of one’s image on other people’s screens is a key factor that feeds into these fatiguing self-presentation concerns during videoconferencing.
Videoconferencing may also increase self-presentation concerns because it allows people to see themselves in real time. A recent correlational study with more than 10,000 respondents showed that Zoom fatigue was associated with mirror anxiety—the stress induced by seeing one’s own live camera feed (Fauville et al., 2021). Additional studies have demonstrated that when people see their own live feed, they tend to fixate on what they perceive as their physical flaws (e.g., Pfund et al., 2020; Pikoos et al., 2021). One survey even found that people who use videoconferencing are more likely to have positive attitudes toward cosmetic surgery (Chen et al., 2021). These elevated self-presentation concerns may explain why experimental research has shown that seeing oneself on camera during a video call decreases team performance on collaborative tasks (Hassell & Cotton, 2017).
For a host of reasons, self-presentation concerns may be less likely to arise in the context of online classes than they are in a typical video meeting. First, the number of participants in a typical online lecture class is far greater than in a typical work meeting. One survey found that work meetings consist, on average, of only nine participants (Russell, 2015)—a more intimate grouping than even a small college seminar. Because the size of one’s video square typically decreases as the number of participants increases, larger virtual gatherings—like college lecture classes—may decrease self-presentation concerns. Second, most college classes tend to have a primary speaker—the teacher—with students playing a less central role as listeners. In this context, a student’s own video feed is likely to appear smaller, both on their own and on others’ screens. Finally, teachers often rely on the screen-sharing feature, which makes visual aids (e.g., lecture slides) rather than participant videos, the main focus of attention.
Most research on videoconferencing has focused on its effect on fatigue, but in the educational context, the impact on engagement is arguably of even greater importance. Increased student engagement is associated with multiple improved learning outcomes (for a comprehensive review, see Fredricks et al., 2004). How, then, can we make online education more engaging? Media richness theory (Daft & Lengel, 1986) dictates that the richer the medium, the more effective it is at communicating information. Face-to-face communication is the richest medium, and communication media decrease in richness as they deviate further from this gold standard (Daft & Lengel, 1986). This may explain why students seem to find online classes less engaging than their in-person counterparts (Peper et al., 2021). Because students are visible to each other when classes are held in-person, having one’s camera on during an online class should approximate the in-person class experience more closely than having one’s camera off. Thus, students may have a richer online class experience when their cameras are on and they can be seen by others. But this theorizing only begs the question of what it is about being seen by others that increases engagement during class.
We propose that being visible increases engagement by making students feel more accountable. When a person has their camera on, the norm is to remain in place, where one can both reach their keyboard and be visible through their camera (Bailenson, 2021). Just as in a physical classroom, a student on camera during a video class may feel sufficient accountability to refrain from simultaneously engaging in tasks such as browsing the Internet, cooking lunch, or folding laundry. Of course, just as in a physical classroom, being on camera is not a panacea, and students can—and likely do—multitask during class. Still, students whose cameras are on should, on average, be more motivated to stay on task, thus feeling more engaged than those with cameras off.
We conducted two field studies with undergraduate students across two university courses. Students in each study were recruited from the same two courses, during different semesters. In Study 1, we investigated students’ actual experiences of fatigue and engagement while they attended classes online in the fall of 2020. Students were assigned to have their cameras on or off at the outset of each class. At the end of class, students reported how engaged they felt during the session and how fatigued they felt as it ended. We expected that camera usage during class would increase engagement. We had no specific predictions about the effect of camera usage on fatigue. In a follow-up within-subject affective forecasting study (Study 2), conducted in the fall of 2021, we investigated students’ perceptions of how their camera usage would affect their engagement and fatigue. At the end of an in-person class, we asked students to imagine the class had taken place virtually; we then asked them to predict their levels of fatigue and engagement if their camera had been on versus off. Given the dominance of Zoom fatigue in the zeitgeist, we expected that students would forecast greater fatigue when imagining themselves with their cameras on. We had no specific predictions about engagement. The data and materials for both studies can be accessed through the Open Science Framework (OSF): https://doi.org/10.17605/OSF.IO/M63SG (Epstein-Shuman & Kushlev, 2022).
During the 2020 fall semester, we recruited N = 65 undergraduate students at a U.S. university from two psychology courses: Health Psychology and Social Psychology. Both courses were large, with an enrollment of at least 30 students.1 Though all students in those classes were required to complete the surveys as part of a class activity, 16 students did not consent to have their data used. Additionally, four students consented but did not complete any surveys. The students completed surveys during multiple class sessions between October 5, 2020, and November 11, 2020, yielding a total of N = 319 observations. The sample size was entirely determined by practical constraints, but post hoc sensitivity analyses with G*Power 3 for the within factor in repeated-measures analysis of variance (ANOVA) indicated that, given our design, our sample size allowed us to detect within-subjects effects as small as Cohen’s f = .18 with 80% power, α = 0.05, two-tailed.
At the beginning of each class, students were assigned to either have their cameras on or off for the duration of a 75-min lecture.2 By employing a within-subjects design, we controlled for prior time spent in online classes and other individual differences. We assigned camera usage to the Social Psychology groups based on the first letters of the students’ first names; students whose first names started with the letters A–J (N = 28) were in Group 1, and students whose first names started with the letters K–Z (N = 21) in Group 2. The Health Psychology students had already been randomly assigned into nine groups to complete collaborative assignments. We combined these nine groups into two larger groups. Group 1 in Health Psychology consisted of nine students, while Group 2 consisted of seven. Overall, our method of camera assignment resulted in slightly more observations in the camera-on condition (N = 180) than in the camera-off condition (N = 139).3
At the end of each lecture, a three-question survey was distributed, using Zoom’s in-meeting polling feature. Since the survey was distributed at the end of class, however, time restraints prevented us from administering the survey in five out of the 19 class periods where camera usage was manipulated. The first two questions were: “How engaged did you feel during class today?” and “How fatigued do you feel right now?” Students responded using a 5-point Likert-type scale ranging from 1 (not at all) to 5 (very much). We used function StatsBy in package psych (Version 2.1.9; Revelle, 2021) in R (Version 4.1.2; R Core Team, 2021) to separately compute correlations between engagement and fatigue between person, r = −.37, p = .003, and within person, r = −33, p < .001. The third question—“Was your camera on or off during this class?”—served as a manipulation check. Out of the 319 observations, students failed the manipulation check on 54 occasions, 14 of which were in the camera-off condition, and 40 in the camera-on condition.4 Thus, the number of actual observations with the camera on (N = 140) was similar to the observations with the camera off (N = 125).
We used restricted maximum likelihood (REML) multilevel models clustered within person to estimate the fixed effect of camera condition while also estimating the random intercept and random effect of condition. The covariance between the random effects was also estimated (see Table 1, for model details). Confidence intervals were calculated using the Wald method, and degrees of freedom were computed by the Satterthwaite method. We computed partial η2 based on the omnibus F test for the fixed effect of condition (Lakens, 2013). These analyses were conducted using Jamovi, Version 1.6 for Mac (The Jamovi Project, 2021).
Effect of Condition (Camera On vs. Camera Off) on Engagement and Fatigue During Virtual Lectures
Intercept [95% CI]
3.51 [3.35, 3.67]
3.35 [3.14, 3.55]
3.30 [3.12, 3.47]
3.26 [3.05, 3.47]
Condition [95% CI]
0.28 [0.10, 0.47]
0.06 [−0.17, 0.29]
Random slope (condition)
Note. Condition was coded using “1” for camera on and “0” for camera off. CI = confidence interval; ICC = intraclass correlation; AIC = Akaike information criterion.
We first used intention-to-treat (ITT) analyses with all 319 observations. While the ITT approach typically provides conservative estimates of condition effects, this approach ensures that confounds, such as individual differences, are not introduced when estimating the effect of condition (Gupta, 2011). By adopting this approach, we also maintain the ecological validity of our findings, since in most situations, instructors cannot force students to have their cameras on or off. In other words, ITT analyses answer the question of whether asking students to have their cameras on or off—regardless of whether students did so—affects engagement and fatigue.
We found that when students were assigned to have their cameras on, they were more engaged during class (M = 3.63, SE = 0.09) than when they were assigned to have their cameras off (M = 3.35, SE = 0.10), b = 0.28, 95% CI [0.10, 0.47], t(56.7) = 2.97, p = .004, F(1, 56.7) = 8.84,
Next, we ran the same REML multilevel models, this time excluding participants who failed the manipulation check. This reduced our original number of observations (Nobs = 319) to Nobs = 265. Comparing times when students actually had their cameras on versus off during class yielded the same pattern of results as the ITT analysis. Here, we again found that students felt more engaged when their cameras were on (M = 3.67, SE = 0.09) versus off (M = 3.32, SE = 0.11), b = 0.36, 95% CI [0.14, 0.56], t(54.5) = 3.26, p < 0.05, F(1, 54.6) = 10.60,
Overall, Study 1 showed that when students were asked to have their cameras on, they were more engaged but not more fatigued than when they were assigned to have their cameras off. A year later, when classes were back in-person, we conducted a follow-up study with students in the same two classes to explore whether students’ expectations of the effect of camera use on engagement and fatigue matched what students in Study 1 experienced. Thus, one goal of Study 2 was to examine whether students expect the benefits of having their cameras on during class for increasing engagement. The second goal of Study 2 was to see if students expect any costs of camera use in terms of feeling more fatigued. If so, our findings would have implications about how likely students are to use their cameras during class when they are not required to do so.
During the 2021 fall semester, we recruited N = 81 students from the same two undergraduate courses as in Study 1: Health Psychology and Social Psychology. We asked students to complete the survey at the end of an in-person class as part of an educational activity. Seven students did not consent to participate in this research—a decision that had no bearing on their grades. Of the 81 students who consented, 18 identified as males, 62 as females, and one student as nonbinary. Participants were largely White (N = 57) and Asian (N = 19). Their ages ranged from 18 to 23, with a mean age of 20. The sample size was entirely determined by practical constraints, but post hoc sensitivity analysis with G*Power Version 3.1 indicated that our sample size allowed us to detect within-subjects effects as small as Cohen’s f = .16 with 80% power, α = 0.05, two-tailed.
We conducted the study when classes had gone back to being in-person after two full semesters of online instruction. At the end of an in-person class, we gave students a one-time survey asking them to imagine that they had taken that same class virtually, on Zoom. We then asked students to predict how they would have felt in two situations: If they had spent the duration of the imagined virtual class with their cameras on and if they had spent it with their cameras off. The order of presentation for these two scenarios was randomized. For each situation, students were asked: “How engaged would you have felt during the class?” and “How fatigued would you have felt at the end of class?” Students responded to both questions using a 5-point Likert-type scale, ranging from 1 (not at all) to 5 (very much). After predicting their engagement and fatigue in both situations, students filled out a demographic questionnaire.
We ran two mixed-level ANOVAs, predicting engagement and fatigue from the camera usage scenario as a within-subjects factor, and the order of scenarios as the between-subjects factor. Consistent with the experiences of students in Study 1, students in Study 2 forecasted that they would feel significantly more engaged during class with their cameras on (M = 3.29, SE = 0.11) versus off (M = 2.05, SE = 0.10), F(1, 79) = 93.34, p < .001,
Contrary to the experiences of students in Study 1, students in Study 2 predicted that having their camera on would leave them feeling significantly more fatigued at the end of class (M = 4.03, SE = 0.11) than having their camera off (M = 2.99, SE = 0.12), F(1, 79) = 64.88, p < .001,
We set out to investigate how students’ camera use during online classes affects both their engagement and fatigue. We found that when students had their cameras on during virtual classes, they experienced increased engagement without increased fatigue. In a field study employing a within-subjects design, students in two online university courses felt more engaged but not more fatigued during classes when they were assigned to have their cameras on versus off. In a separate study conducted a year later, students taking the same two courses in-person predicted they would feel more engaged if they had taken the class online with their cameras on instead of off. These same students, however, also predicted that they would feel more fatigued if they had their cameras on.
The present research is the first to explore the effect of camera usage on fatigue in an educational context. Several previous studies have documented the effect of camera usage in the work domain (Fauville et al., 2021; Shockley et al., 2021). The existing evidence suggests that a person’s camera usage during a work meeting does increase fatigue (Shockley et al., 2021). Correlational findings further suggest that this effect may be due to the increased cognitive demand associated with elevated self-presentation concerns that arise when people are on camera (Fauville et al., 2021). The differences between those findings and ours may be due to myriad factors, such as the age of participants; university students, on average, are younger than employees. We propose, however, that the absence of an effect of camera usage on fatigue may be due, at least in part, to lower self-presentation concerns in the context of online lecture classes, which tend to include more participants than a typical work meeting. If this is the case, then the number of participants in a videoconference session may be a critical factor that affects fatigue across contexts. In smaller, discussion-driven classes, for example, students’ camera usage may lead to greater fatigue, just as it does for employees in a typical work meeting (Shockley et al., 2021). Conversely, in larger work meetings, especially those that focus on a presentation rather than on participant video feeds, camera usage may increase engagement without leading to greater fatigue, just as it does for students in larger lecture-based classes.
If camera usage leads to fatigue by increasing self-presentation concerns, it may be possible to mitigate this effect directly by changing the prominence of one’s own video feed (cf. Bailenson, 2021). For example, software like Zoom allows participants to alter their settings so that they do not see their own video feed. Future research should explore whether people who know they are on camera but do not see their own video feed are more or less preoccupied with how they appear to others, and thus more or less fatigued. In addition, self-presentation concerns may be driven by the perception of how one’s video feed appears on other people’s screens. In Zoom’s “grid view,” for example, participants’ video feeds appear the same size, regardless of who is speaking. If a company suggests that all their employees set their Zoom calls to grid view, participants may be less concerned with how they appear to others, and thus less fatigued.
Students in Study 1 were more engaged but not more fatigued when they had their cameras on versus off. Students in Study 2 expected that having their cameras on during an online class would make them both more engaged and more fatigued. One interpretation of this pattern of findings is that students appreciate the benefits but overestimate the cost of having their cameras on during online classes. If this is the case, students may prefer to keep their cameras off during online classes to avoid the perceived effect of camera use on fatigue. Consistent with this possibility, students in Study 1 were three times more likely to keep their cameras off when assigned to have them on than vice versa. It is also possible, however, that the discrepancy between experienced and forecasted fatigue is attributable to differences in how students actually felt after a year of pandemic-driven isolation. If camera usage becomes more fatiguing over time, we might have found effects on experienced fatigue if our Study 1 had occurred in fall 2021, when the students in Study 2 made their predictions. Regardless of whether students correctly or incorrectly believed that having their cameras on during class would make them more fatigued, however, future research should explore whether the perception that camera usage increases fatigue leads students to keep their cameras off at the expense of educational engagement.
One of the key strengths of the present research is that we were able to test the effect of camera usage in the real world, increasing the ecological validity of our findings. To make the studies possible in a field setting, however, we were not able to assess the proposed mechanisms behind the effects of camera usage on fatigue and engagement. Future research should thus examine how camera usage affects cognitive load, self-presentation concerns, and accountability. In our field research, we also had to rely on self-report measures of engagement and fatigue. Future laboratory research should explore whether these findings hold when engagement is objectively measured through metrics such as eye tracking. Future research should also explore the downstream consequences that class engagement due to camera usage may have for objective learning outcomes. In addition, our sample size was determined by practical factors, such as the number of students in the two courses, rather than based on a priori power analyses. Thus, we cannot claim that our study was powered to provide evidence of the absence of an effect (Altman & Bland, 1995). Still, we note that the effect size we observed of camera usage on fatigue was extremely small and thus unlikely to be of any practical significance (Funder & Ozer, 2019). Finally, we were not able to examine whether the effects of camera usage depend on gender, race, or age. Past studies with working adults have shown that videoconferencing may be more fatiguing to women than to men, possibly because of higher self-presentation concerns in women (Fauville et al., 2021; Shockley et al., 2021). It is not clear, however, whether these gender differences would replicate in the context of online classes.
Several factors limit the generalizability of the present research. First, our study focused on a limited population: College students enrolled in two psychology courses at a selective private university, located in a large metropolitan area of the United States. Thus, despite their high ecological validity, our findings may not generalize to other populations: Students who attend different educational institutions or who are taking courses in different subject areas, may have different experiences. Second, we conducted our studies amid a once-in-a-lifetime pandemic that limited face-to-face interactions of any kind (World Health Organization, 2020). In the unique context of social isolation necessitated by the COVID-19 pandemic, camera usage may have been more energizing than it would be under normal circumstances. Finally, this research focused on a classroom that was newly online. Future studies could assess whether the effects of camera usage on fatigue and engagement depend on how long students have already been participating in online learning.