Students’ Motivation in an Online and a Face-To-Face Semester
A Comparison of Initial Level, Development, and Use of Learning Activities
Abstract
Abstract: Challenges for university students were high during distance education in lockdowns due to the COVID-19 pandemic. Self-regulation and motivation became more important, but motivation was possibly challenged more. To investigate motivational differences and possible positive effects of evidence-based learning activities, we followed two cohorts of preservice teachers over the course of one semester: One cohort was followed in 2019 in a face-to-face semester (N2019 = 225), and another cohort was followed 1 year later during the first lockdown (N2020 = 311). Students indicated their motivation at five measurement occasions and reported their use of learning activities twice. Multigroup linear change models indicated an overall decline of motivation in both cohorts. Surprisingly, neither initial motivation level nor motivational change differed between cohorts. Students who used more learning activities reported a more positive motivational development. This highlights the chance of evidence-based learning activities for students’ motivation in regular and distance education.
In March 2020, the lockdown due to the COVID-19 pandemic challenged learners and teachers to adapt to distance education. First empirical research indicates that distance education came along with deleterious effects for students’ well-being (Kedraka & Kaltsidis, 2020; Schwinger et al., 2020; Steinmayr et al., 2022). In higher education, many instructors converted their courses into online formats and, for example, made self-study materials available to students online. This required a high degree of self-regulation on the part of the students when they wanted to learn successfully. At the same time, students reported problems starting online classes on their own and staying focused (Wong, 2023). Correspondingly, Lockl et al. (2021) reported that distance learning required students to engage in higher levels of self-regulation.
In this context, motivation to learn is particularly important because it is a crucial prerequisite for self-regulated learning (see, e.g., Pintrich, 2004). To self-regulate learning successfully, students need motivation to initiate learning and also to maintain motivation over time. However, several researchers have found students’ motivation to decline across different developmental phases and time spans (e.g., Benden & Lauermann, 2021; Jacobs et al., 2002; Kosovich et al., 2017; Robinson et al., 2019). In online courses during pandemic-related lockdowns, motivation was possibly threatened more because many prerequisites for motivation were missing (e.g., social presence and interaction; see also Müller et al., 2021) and students reported an increased amount of stress (Usher et al., 2021). The question arises as to what differences emerge in students’ motivation during distance learning compared to face-to-face learning.
Extending the question of motivational challenges during distance education, it is important to examine what helps students stay motivated, be it in distance learning or face-to-face learning. One promising idea to help students stay engaged and motivated is offering evidence-based learning activities. Given the challenges of pandemic-related distance learning, learning activities that can be easily implemented in an online course, in particular, offer an opportunity to facilitate distance learning for students. Activities that help students space out their learning, test their knowledge repeatedly, and receive feedback on their performance have already been shown to boost students’ performance (e.g., Dunlosky et al., 2013). Relying on a supply-use model, prior research has shown that, in a regular semester, the use of such activities explained students’ performance even beyond prior achievement and motivation (Bosch et al., 2021). The present study investigates whether the use of such activities is associated with a more favorable motivational development, both in a regular learning context and in a distance learning context.
Student Motivation
In our research, we are interested in university students’ intrinsic motivation in terms of the intrinsic value component from expectancy–value theory (EVT; Eccles et al., 1983; Wigfield & Eccles, 2000). Intrinsic motivation describes the pleasure and interest with which one approaches a topic or task. Together with other motivational values and performance expectations, it is supposed to influence achievement-related behavior and choices (Eccles & Wigfield, 2020). Intrinsic motivation is important in education for two reasons. First, empirical research shows that students who have high intrinsic motivation and performance expectancy (expectancy component of EVT) tend to learn more and perform better (e.g., Kriegbaum et al., 2018; Robinson et al., 2019; Trautwein et al., 2012). They also tend to drop out from university courses less frequently (Benden & Lauermann, 2021). Second, intrinsic motivation is also a desirable outcome of learning processes, as students should leave higher education courses with high motivation to learn and strong interest in the field.
Unfortunately, students suffer from motivational challenges. Longitudinal studies show that students’ motivational values for various school subjects decrease overall from first to twelfth grade (e.g., Jacobs et al., 2002). A negative trend in student motivation was also found through the first 2 years of college in one study (Robinson et al., 2019). Even over a relatively short period of time, such as an academic semester, student motivation develops negatively (Benden & Lauermann, 2021; Kosovich et al., 2017). Negative trends in motivation can have negative consequences for students. One serious consequence of too little motivation is when students fail to achieve their goals and drop courses (Benden & Lauermann, 2021) or even university. In Germany, about 25% of students drop out from university before completing their bachelor’s degree (Heublein & Wolter, 2011; OECD, 2013) and lack of motivation was identified as an important predictor of dropout, along with poor performance and financial problems (Heublein & Wolter, 2011).
In summary, there are many reasons why students should stay motivated. However, they seem to face motivational challenges during their academic careers. During the lockdown and distance learning, more challenges probably came along. Demands on students have increased, as they should take more responsibility for their learning process and become more self-directed learners (Lockl et al, 2021). They had to independently initiate learning sessions, stay focused alone in front of their electronic device, and structure their day. Not all students were able to cope well with these requirements (Lockl et al., 2021; Wong, 2023). With increased challenges and stress, students’ motivation could have suffered more during distance education. Under an EVT perspective, Wang and Eccles (2012) investigated classroom characteristics that facilitate students’ intrinsic value for a task. They found those characteristics helpful for students’ intrinsic task value that support feelings of competence, connectedness, and autonomy, concepts from self-determination theory (SDT; Ryan & Deci, 2020). According to SDT, a crucial prerequisite for motivation is satisfaction of the basic psychological needs for relatedness, autonomy, and competence (Ryan & Deci, 2020). Because social interaction was diminished during distance education, the need for relatedness was possibly frustrated in many students. Wong (2023), for example, found that especially learners’ need for relatedness was frustrated in online learning, but not autonomy and competence. Such a frustration of one need may contribute to a decreased motivation in distance education. However, because temporal flexibility and spatial flexibility were increased in online courses, the need for autonomy could have been satisfied more at the same time. How such frustration of one need (relatedness) can be compensated by increased satisfaction of another need (autonomy), and how the different needs differ in strength and importance at the individual level are as yet open questions (e.g., Vansteenkiste et al., 2020). However, given the many changes and challenges students faced and the increasing stress, it would stand to reason that their motivation suffered. Indeed, first empirical research suggests detrimental effects of pandemic-related distance education on university student motivation. In an exploratory study, Usher et al. (2021) retrospectively surveyed N = 358 US university students at the end of the spring 2020 semester about their experiences and motivational changes. The majority of students reported that they procrastinated more and were less motivated during lockdowns. In another study, students retrospectively reported that courses that were moved online due to the pandemic were less enjoyable and less interesting (Garris & Fleck, 2022). However, the authors could not compare motivation before and after the change to distance education directly. Müller et al. (2021) surveyed two cohorts of students, one before and one during forced distance learning. Using a self-determination approach, they found that less desirable forms of motivation increased during lockdown. However, these studies could not look at the development of students’ motivation over time. We are not aware of any study to date that has directly compared the intrinsic motivation and its development of higher education students before and after switching to distance education.
The well-documented decline in motivation over time, as well as additional challenges during lockdowns and the potentially increased threat to motivation, as a result, leads to the question of what might be done to mitigate negative motivation trends.
Antecedents of Motivation
What could help students staying motivated during regular and online semesters? Motivation is most often looked at as an antecedent of behavior, but behavior is also an antecedent of motivation, as described in several theoretical frameworks. In the model of situated EVT from Eccles and Wigfield (2020), for example, previous achievement-related experiences are thought to influence motivational values. Also, in models of self-regulated learning (e.g., Schmitz & Wiese, 2006), every learning action is followed by a reflection of outcomes. The results of this reflection are then integrated to build motivation for the next learning action. Hidi and Renninger (2006) argue that evoking situational interest makes students engage repeatedly, thereby strengthening individual interest in a certain domain more generally.
In their meta-analytic review, Credé and Kuncel (2008) investigated predictors of academic success in higher education and identified study habits, skills, and attitudes as an important pillar for academic success. Both motivation and study skills and habits explained performance beyond cognitive predictors. But how these two predictors interact is not that clear yet. Interestingly, Credé and Kuncel (2008) propose a model of academic success that implies behavior (study habits and attitudes) as an antecedent of motivation (p. 430). This relationship, however, is not further specified or investigated empirically. In another very recent review of meta-analyses, Jansen et al. (2022) investigated the antecedents of K-12 students’ motivation. They report that students’ previous achievement and instructional practices were related to students’ motivation with medium-order effect sizes. However, they also note that the antecedents of higher education students’ motivation have not been as widely studied. As mentioned earlier, a key challenge for higher education students is maintaining motivation over time in a self-directed learning environment, and this challenge can be even more demanding in distance education.
Taking behavior as an antecedent of motivation in a self-regulated learning context, we hypothesize that students’ active learning behavior is associated with sustained motivation over time. In particular, we expect regular participation in learning activities to help students maintain intrinsic motivation in the subject.
Evidence-Based Learning Activities
What could instructors do to support students’ engagement and motivation? According to the supply-use model for learning and instruction in higher education (see Figure 1), instructors should provide learning opportunities that students can use to acquire knowledge (Bosch et al., 2021). When teachers think about which learning opportunities to offer, they should rely on evidence-based activities that help students to learn effectively (Dunn et al., 2013). The typical design of a lecture class includes regular lecture sessions. If students regularly attend sessions, they tend to receive better grades in university (Credé et al., 2010). Next to lecture sessions, empirical research has found that learning activities that help students space out their learning, test their knowledge repeatedly, and receive feedback on their performance promote student learning and performance (Downs, 2015; Dunlosky et al., 2013). For example, practice tests of the learning content allow students not only to assess their learning but also enhance students’ memory for the learned and for new material (e.g., Carpenter, 2012). The effect of testing is even stronger when students receive feedback on their performance (Phelps, 2012). Much research on testing focuses on multiple-choice knowledge tests (e.g., Cogliano et al., 2019). Other useful test activities include writing assignments that require students to apply, integrate, and reflect on the content (Balgopal et al., 2012; Dunn et al., 2013). Furthermore, the principle of distributed practice is related to students’ performance (Dunlosky et al., 2013). Consequentially, activities should be effective if they allow students to space their learning over a period of time.
Based on these empirical results, a university lecture class was designed to promote student engagement. The lecture class included regular practice tests distributed over time in the form of knowledge tests with confidence-weighted true–false items (see Dutke & Barenberg, 2015), writing assignments, and feedback on these test activities. Such a lecture design has already been investigated empirically in the field, and students who engaged in more activities performed better at the end of the course (Bosch et al., 2021). However, regular engagement in these evidence-based activities may not only improve students’ knowledge but also help them to stay motivated by helping them discover interesting and joyful aspects of the content.
Summary and Research Questions
First empirical evidence suggests that students’ learning motivation has suffered during distance education in the COVID-19 pandemic. The aim of the present research is to compare students’ motivational level at the beginning of a semester and the development over the course of one semester in a regular face-to-face and an online lecture class. Given the motivational challenges in self-regulated learning contexts, we further investigate evidence-based learning activities as a tool that may help students stay engaged and motivated even in an online lecture class.
- -H1: We expect to replicate the finding that students’ intrinsic motivation declines over the course of one semester, regardless of the lecture design (present or online).
- -H2: We further expect that initial intrinsic motivation is lower and the decline is steeper in an online semester than in a regular face-to-face semester.
- -H3: We hypothesize that students who more often attend lecture sessions and use evidence-based learning activities (practice tests in the form of self-tests and writing assignments) have smaller motivational declines.
Exploratively, we investigate which learning activities may be particularly important and offer a chance to improve teaching and learning, especially in online courses. Moreover, we analyzed which was the better predictor of students’ dropout: use of learning activities or motivation early in the semester.
Method
To test the hypotheses stated above, self-report data from two cohorts of preservice teachers were analyzed and compared.
Procedure
The setting of the study was an introductory lecture class on educational psychology for preservice teachers at a German university. This lecture class is obligatory for preservice teachers. Cohort 1 was followed during summer term 2019 in a regular face-to-face semester. When the pandemic-related lockdown in March 2020 forced teachers and learners to convert to distance education, the design of the lecture class was left the same, except for the weekly lecture sessions which switched to online. Cohort 2 was followed during this online term in summer 2020.
In both cohorts, lecture content and evidence-based learning activities were the same. The lecture class included weekly lecture sessions (face-to-face 2019 and online 2020) and practice tests in the form of self-tests and writing assignments online. All activities were optional. However, we recommended students to engage in as many activities as possible. On five self-tests, students were quizzed on the lectures’ topics of the last three weeks with confidence-weighted true–false items (Dutke & Barenberg, 2015) and received feedback on their performance (number of correctly answered questions). With up to eight writing assignments, students had the opportunity to reflect on the content of the lecture and apply it to a new problem. Students received individual feedback on these assignments. The practice tests were offered every week during the semester and thereby helped students learn continuously and space their learning. The design of the learning activities is described in further detail by Seifried and Spinath (2020).
For data collection in both cohorts, all students who registered for the course were invited to complete online surveys after each self-test, starting after the first lecture session and from this point on every three weeks. Each time, students were asked to indicate their intrinsic motivation. In the middle (T3) and at the end of the semester (T5), class attendance and use of optional evidence-based learning activities were assessed.
At several time points, we further assessed scales that were related to the evaluation of the lecture or illustration of the lectures’ content and thus were not used in the present study. Participation in the surveys was voluntary and anonymous (students generated anonymous codes for longitudinal monitoring). Students were informed about the content and aims of the survey and that they would have no disadvantage if they did not participate.
Sample
At the beginning of the semester, N2019= 225 (N2020= 311) preservice teachers took part in the first online survey. Of those, n2019= 121 (n2020= 119) students took part in the survey at T5. Participation rates in surveys for each measurement occasion are depicted in Table 1. In 2019, the age of the participants was on average M = 21.44 (SD = 2.36) years (2020: M = 21.55, SD = 2.86), and 66.2% were female (2020: 64.6%). In 2019, students stated that they had been studying for M = 4.21 (SD = 2.36) semesters (2020: M = 4.28, SD = 2.39) and indicated studying a wide variety of teaching subjects.
Measures
Next to the demographic data reported above (see sample), we assessed the following variables for our study:
Intrinsic Motivation for Educational Psychology (T1–T5) was assessed with three items adapted for the “educational psychology” context instead of “school” (e.g., “What I learn in educational psychology is interesting to me”) from the Scale for Assessing Subjective School-related Values (SESSW; Steinmayr & Spinath, 2010). Students were asked how each statement applied to them from 1 (not at all) to 5 (very much). Cronbach’s α indicated good reliability of the scale at each measurement occasion (α = .85–.99).
Use of Learning Activities (T3 and T5) was assessed with three items, each concerning a learning activity that could be used during the semester: regularly submitting writing assignments, regularly attending the lecture sessions, and taking self-test. For use of the activities, writing assignments and lecture attendance, students were asked how much they had used them on a scale from 1 (not at all) to 5 (very much). For use of the activity, self-tests, students were asked the number of times they had participated in self-tests (1–5 at T5 and 1–3 at T3). For use of learning activities at T5, a mean was computed.
Data Analysis
All models were estimated with Mplus 8.0 (Muthén & Muthén, 1998–2017). Hypotheses were investigated using a multigroup second-order linear change model. Within this framework, intrinsic motivation at each time point was modelled as a latent state factor with three indicators each time. Furthermore, second-order change factors (level and change) were estimated to load on latent-state motivation factors. The level factor had loadings of one on all latent-state factors of intrinsic motivation (T1–T5), and the change factor had loadings of 0, 0.3, 0.6, 0.9, and 1.2 on latent-state factors of intrinsic motivation T1–T5, respectively, because surveys took place every three weeks. This modeling approach accounts for measurement errors and bias in parameter estimates at the item and construct levels (Grimm & Ram, 2009). We evaluated model fit with the comparative fit index (CFI; Bentler, 1990), the root mean square error of approximation (RMSEA; Steiger, 1990), and the standardized root mean square residual (SRMR; Kline, 2011). According to Hu and Bentler (1999), a CFI higher than .95 and RMSEA as well as SRMR lower than .08 indicate good fit.
Before applying the growth model, we tested for longitudinal measurement invariance to make sure we can compare factor means of intrinsic motivation across measurement occasions. To do so, strong measurement invariance is a prerequisite, which means that intercepts of manifest indicators and factor loadings need to be invariant over time. To test for strong invariance, we compared models of strong invariance with models with released constraints and evaluated if the latter showed meaningful better model fit. According to Chen (2007), a meaningful improvement of model fit when releasing intercepts is indicated by an increase of CFI ≥ .005 and, at the same time, a decrease of RMSEA of ≥ .010 or a decrease of SRMR ≥ .005. When releasing factor loadings, an increase of CFI ≥ .005 needs to be accompanied by a decrease of RMSEA of ≥ .010 or a decrease of SRMR ≥ .025.
To investigate motivational change, we estimated a multigroup linear change model for intrinsic motivation and inspected estimates of the change factors (H1). Next, we compared estimates for latent growth factors to evaluate differences between cohorts (H2) and entered students’ mean activity use as a predictor of the change factor to investigate the association of evidence-based activities with motivational change (H3).
Results
Descriptive data for intrinsic motivation and uses of learning activities in both cohorts are depicted in Table 2.
On a descriptive basis, students began the semester with relatively high intrinsic motivation. Although scores decreased over time, motivation remained high in both cohorts (see Table 2; Figure 2).
Preliminary analyses indicated strong measurement invariance over time. The model assuming strong invariance had a good model fit (CFI = .992; RMSEA = .024; SRMR = .040). Releasing invariance constraints of intercepts led to no meaningful improvement in model fit (CFI = .995; RMSEA = .020; SRMR = .036; according to Chen, 2007). Also, releasing invariance constraints of factor loadings led to no meaningful improvement in model fit (CFI = .995; RMSEA = .020; SRMR = .025). All further analyses were conducted with the model of strong measurement invariance.
The multigroup linear change model had an acceptable model fit (CFI = .965; RMSEA = .048; SRMR = .092). Standardized factor loadings were all above .74 (ps < .01) for observed motivation values loading on the corresponding latent motivation factor (T1–T5). Means of second-order change factors in both cohorts were negative and significantly different from 0 indicating a decline of intrinsic motivation. Furthermore, variances of level and change factors in both cohorts were significantly different from 0 indicating interindividual differences in motivation at the beginning and in the motivational development over the course of the semester (see Table 3).
Means’ 95% confidence intervals of level and change factor between cohorts overlapped indicating no significant differences in motivation at the beginning or motivational development in 2020 compared to 2019.
When adding the mean of students’ learning activity use over the course of the semester (T5) as a manifest predictor of the change factor, controlling for its correlation with the level factor at the same time, model fit remained acceptable (CFI = .964; RMSEA = .045; SRMR = .106). Students’ mean use of all learning activities was positively associated with change of intrinsic motivation in both cohorts (r2019 = .489, p < .01; r2020 = .453, p < .01). Looking at lecture attendance on the one hand and additional activities (self-tests and writing assignments) on the other hand, the following results were obtained: The use of additional activities was associated with motivational change in 2020 (r2020 = .411, p < .01), but not in 2019 (r2019 = .232, p = .118). However, constraining the correlation coefficients to be equal across groups did not change model fit significantly (Δχ2[1] = 0.049, p = .825), indicating that the coefficients between groups did not differ significantly. When looking only at lecture attendance, this activity explained variance in motivational development in a face-to-face semester (r2019 = .567, p = .017), but not in an online semester (r2020 = .051, p = .704). Constraining these coefficients to be equal across groups led to a significantly worse model fit (Δχ2[1] = 6.485, p = .011), indicating that the coefficients between groups differed significantly.
T-tests with an adjusted α < .01 (Bonferroni corrected) were computed to compare students who dropped out before T5 and who completed all surveys. We neither found differences in intrinsic motivation (T1–T4) in either cohort (t[106–309] = −0.213 to 2.092, all ps > .01) nor differences in class attendance at T3 (2019: t[106] = 0.485, p > .01; 2020: t[159] = 2.168, p > .01). However, students who dropped out until T5 already reported significantly less submissions of writing assignments at T3 in cohort 2020 (t[158] = 4.507, p < .01, d = 0.81, 95% CI [0.469, 1.148]) and significantly less participation in self-tests both in cohort 2019 (t[226] = 8.850, p < .01, d = 1.17, 95% CI [0.892, 1.455]) and in cohort 2020, t(309) = 12.048 , p < .01, d = 1.41, 95% CI [1.151, 1.659].
Discussion
Different from what was expected in the face of the COVID-19 pandemic and possible motivational challenges, students’ initial intrinsic motivation and change of intrinsic motivation did not differ between a face-to-face semester and an online semester. Surprisingly, in both cohorts, students’ motivation was high at the beginning of the semester and stayed high, instead of the overall decline. Other authors have reported detrimental changes in students’ motivation (Müller et al., 2021). However, the authors investigated the decreased possibilities of need satisfaction (e.g., peer interaction to feel related) relying on Ryan and Deci’s (2020) self-determination approach. Need satisfaction can be considered a prerequisite for intrinsic motivation. However, intrinsic motivation can be explained by more factors and the learners in our study maybe were able to cope with motivational challenges. In addition, the study by Usher et al. (2021) retrospectively asked students about motivational changes. Considering that other variables such as student well-being have suffered (e.g., Schwinger et al., 2020; Steinmayr et al., 2022), students’ retrospective data on motivational change may be biased. In our study, we were able to compare students of the same lecture class from 2019 to 2020 with the same measures of intrinsic motivation. The sample in both cohorts was comparable regarding their sociodemographic characteristics, and the lecture content did not differ between cohorts. This allowed us to compare the change in motivation directly within one model, and we could not find motivational differences between cohorts. One explanation could be that students who experienced a sharp drop in motivation right at the beginning in 2020 dropped out of the data collection. However, because motivation did not differ between cohorts at baseline, other explanations are possible. Schwinger et al. (2020) reported that it was primarily the decrease in autonomy during the lockdown that led to a decrease in well-being. It is possible that our students experienced no loss of autonomy within the lecture and were able to focus on lecture content and engage in more learning activities because constraints such as social distancing reduced other activities and sources of satisfaction. Another possible explanation for the comparable motivational development lies in the evidence-based activities themselves. Perhaps these activities have made distance learning more attractive in the context of this lecture. This would illustrate the possibilities of evidence-based learning activities in distance learning.
Students in both cohorts seemed to suffer from motivational challenges reflected in a motivational decline and dropout. Recently, Benden and Lauermann (2021) highlighted the importance of short-term motivational changes for performance and course dropout in university. The motivational decline we found highlights a challenge for university students and instructors that is present both in face-to-face courses and in distance education. When interpreting the decline in motivation, it should be considered that motivation was quite high at the beginning of the semester. In addition, almost everyone probably knows the feeling that working on a long-term project – whether it is successfully attending a lecture as a student, teaching a lecture as an instructor, or working on any project – challenges motivation from time to time. Motivational problems do not mean that one is certain to fail. How one deals with challenges is critical.
To encounter students’ motivational challenges, evidence-based learning activities may be a suitable tool. Despite from being helpful for students’ performance (Bosch et al., 2021), in this study, they were related to a more positive development of motivation. The associations of learning activities and motivational change were moderate to large in size. Of course, motivation is also important to engage in learning activities. We argue, however, that the continued engagement may also help to maintain motivation over time because learning behavior is not only a consequence but also a prerequisite as many models of motivation include past behavior as a precursor of motivation (e.g., Eccles & Wigfield, 2020). Evidence-based learning activities can be considered a chance for university instructors and students because they could be easily adapted to different contexts and are associated with students’ motivation even in pandemic-related distance education. In both cohorts, students could attend the lecture (in presence in summer 2019; online in summer 2020) to get to know the content and to ask questions. This activity probably differed the most between 2019 and 2020. Explorative analyses revealed that in 2020, additional evidence-based activities (writing assignments and self-tests) were associated with a more favorable development of motivation. Given the reduced interaction between students and instructors and less feedback in distance education, individual feedback on tests and the more structuring character of the activities (particular time slots for each activity) might have gained importance for students’ motivation.
Overall, dropout from surveys and also from the course was high, especially in 2020. One reason for the higher dropout rate in 2020 could be that more students visited the first lecture session and enrolled in the course (N = 311 compared to N = 225 in 2019) because it was easier online, and they may not have had strong intentions to complete the course. Furthermore, students who submitted more writing assignments and participated in more self-tests completed all surveys and probably the course with a higher probability. This points to another important aspect of evidence-based learning activities. They do help students learn the content, they are related to a more favorable motivational development, and they may also help students finish a course (maybe by preventing them from drastic motivational declines).
Our study is the first that compared student motivation and its development over the course of one semester in a face-to-face lecture course and immediately after the change to distance education and associations with evidence-based learning activities. There are, of course, limitations that need to be considered. First of all, we cannot draw causal conclusions about the effect of learning activities due to the nonexperimental design. However, applying a longitudinal design gives us first ideas how motivational challenges in higher education could be encountered. Furthermore, it is likely that our findings are limited to certain contextual conditions. Our study was conducted in a large lecture class with specific evidence-based learning activities. In lecture classes, even in a face-to-face semester, social interaction is usually not as lively as in seminar formats. Therefore, the discrepancy in social interaction and maybe also motivation might have been more pronounced in seminar formats. However, the study of large lecture classes in distance education and the ways in which evidence-based activities can be implemented here are of great importance to educational practice, as large lecture classes are a common format in undergraduate curricula. The generalizability of these findings to other student groups and the further development of motivational trends in ongoing distance education are beyond the scope of this study and would be of interest for future research.
In addition, we had to rely on self-report data for the study variables. The assessment of learning activities might be better if they were collected objectively (e.g., the number of essays a student submitted). In this study, we had to rely on self-reports because of anonymous data collection. However, we asked about specific activities, the survey was voluntary and anonymous, and students did not face any consequences in case they did not participate, so we assume that they answered truthfully.
Considering dropout rates, our results could be biased because more motivated students participated in more surveys. However, we still had some variability in our sample regarding motivation, and our analyses indicated that students who participated in all surveys did not differ in motivation from those who dropped out.
Conclusion
With these limitations in mind, our study yielded noteworthy results. In contrast to the results of other empirical studies, student motivation does not seem to have suffered more in our case with distance learning than with face-to-face learning. One aim was to explore chances to deal with motivational challenges that students experience over the course of one semester. In the case of distance learning and face-to-face learning, we highlighted the opportunities for teachers to provide useful and potentially motivating activities. This could help motivate students by keeping them engaged continuously.
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