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Article

Synchronous Distance Learning: Effects of Interest and Achievement Goals on Police Students’ Learning Engagement and Outcomes

by
Markus H. Hefter
1,* and
Holger Nitsch
2
1
Department of Psychology, Bielefeld University, 33615 Bielefeld, Germany
2
Department of Police, University of Applied Sciences for Public Administration in Bavaria, 82256 Fürstenfeldbruck, Germany
*
Author to whom correspondence should be addressed.
Educ. Sci. 2024, 14(2), 118; https://0-doi-org.brum.beds.ac.uk/10.3390/educsci14020118
Submission received: 15 December 2023 / Revised: 16 January 2024 / Accepted: 22 January 2024 / Published: 24 January 2024
(This article belongs to the Special Issue Online and Distance Learning during Lockdown Times: COVID-19 Stories-Series 2)
Browse Figure
Versions Notes

Abstract

:
Online learning has boomed, especially in synchronous settings. Questions remain open regarding the influence of interruptions and learner factors such as interest and achievement goals on learning engagement and outcomes. To address these questions, the present field study relied on a synchronous online course and evaluated it with a sample of 136 police students (Mage = 29.58 years). Not only did the online course foster students’ self-efficacy, it was also given similarly high evaluations to previous offline iterations of the course. Furthermore, the students’ self-perceived learning gains correlated highly with actual test performance. Their interest was a positive predictor of these learning gains, whereas work avoidance goals were a negative predictor of learning gains. Learning engagement mediated these effects. Finally, learning outcomes and interruptions were negatively associated. Our results imply that instructors should consider interest and achievement goals as important learning predictors, as well as the detrimental effects of interruptions on learning outcomes.

1. Introduction

Online distance learning has boomed in recent years. The COVID-19 regulations forced teachers and instructors to switch quickly from traditional in-person curricula to distance learning settings [1,2]. Such distance learning settings occur basically in a synchronous or asynchronous format [3,4], each format with its own characteristics, benefits, and limitations [5]. Considering the relation between the instructor and student regarding both time and place, synchronous learning simply means same time but different place. Thus, the learning experiences and interactions between instructors and students are live and in real time. Common methods of choice are videoconferencing platforms such as Zoom or MS Teams. Asynchronous learning on the other hand means different time and different place. Hence, the learning experiences and interactions between instructors and students are not live and in real time. As instructors and students need not be online at the same time, common formats rely on prerecorded video lectures (see e.g., [6]).
Asynchronous settings may have certain benefits, such as reducing many limitations of place and time. However, due to the absence of an instructor, asynchronous settings can also bear risks, such as unsupervised students engaging in off-task behavior [7]. Even with an instructor present, students are prone to engaging in off-task behavior during class (e.g., over 50% of students engaging in texting), as Burak’s surveys [8] revealed as long as a decade ago. The numbers are even higher of off-task behavior during online courses. Similar to off-task behavior, interruptions can have various and detrimental effects on learning processes. Those negative consequences can be considered from cognitive, motivational, and affective perspectives (see e.g., [9]). From the perspective of learners’ limited cognitive resources [10,11], interruptions naturally harm learning because they exploit and distract cognitive resources from the actual learning task. Unsurprisingly, the number of interruptions was a negative predictor of learning outcomes in asynchronous distance learning scenarios in recent studies [6,12].
In contrast, synchronous settings feature an instructor’s presence. Synchronous distance learning thus enables immediate interaction between instructors and students [13]. Such interaction can have positive effects on learner satisfaction [14]. Furthermore, and more in the present paper’s focus, such interaction can encourage mental activity. Mental activity is obviously key for meaningful and lasting learning, while mental passivity on the other hand, is not. Renkl and colleagues [15,16] encapsulated this central tenet in a stance they call Active Processing. Chi and Wylie [17] argue likewise, when they described four different modes of learners’ engagement in their ICAP framework: Interactive, Constructive, Active, and Passive. They predict that learning will improve by increasing students’ engagement, from passive to active to constructive to interactive. Passively receiving a video lecture and not going beyond “watching the video without doing anything else” [17] (p. 221) is thus inferior to interactive activities such as asking and answering questions.
In addition to such benefits, synchronous distance learning via videoconference platforms bear some risks as well. For instance, the term “Zoom Fatigue” refers to the exhausting nature of intense and hour-long video conferencing [18,19]. Bailenson [20] offers aspects of nonverbal overload as explanations, such as eye gaze at close distance, or reduced mobility. Furthermore, there is the phenomenon of black boxes [21], meaning that students switch off the cameras and remain passive and non-responsive. Looking at a grid of black rectangles [13], the instructor thus gets hardly any feedback and information about the students’ learning engagement.
Against this background of the benefits and risks of asynchronous and synchronous distance learning settings, the present field study evaluated a synchronous online course targeting learning engagement and gains. In light of the aforementioned Active Processing stance and the ICAP framework, this course should focus on the students’ learning engagement.

2. Topic Interest and Achievement Goals—Influencing Learning Engagement and Gains

Given the importance of students’ engagement for learning, it would make sense to look at learner factors capable of seriously influencing that engagement. In this paper, we focus on two important motivational learner factors, namely Topic Interest and Achievement Goals.
Starting with the concept of topic interest, we build on its definition “as a content-specific motivational characteristic composed of intrinsic feeling-related and value-related valences” [22] (p. 299). Briefly put, topic interest refers to the “level of interest triggered when a specific topic is presented” [23] (p. 545). A large body of research discussed its conceptualization and underscored its importance for learning and comprehension [22,23,24,25]. More recent research also identified topic interest as a protecting factor against off-task-thinking (i.e., mind wandering) during online video lectures [26]. Consequently, topic interest should be associated positively with learning engagement and learning gains.
Besides topic interest, achievement goals have also been the subject of decades of research in educational psychology [27,28,29]. Achievement goals are defined as “future-focused cognitive representation[s] that guide[s] behavior to a competence-related end state that the individual is committed to either approach or avoid” [28] (p. 423). These representations are a powerful and important motivational basis in that they influence affection, cognition, and behavior [30]. There are manifold conceptualizations and operationalizations of achievement goals (for an overview, see [28]). For the present paper, we were interested in whether and, if so, how learner factors such as achievement goals might influence synchronous online learning. Hence, and to limit this study’s item numbers to a relevant, small core, we chose two classes of goals we consider meaningful for a distance learning setting: Learning Goals and Work Avoidance Goals.
Learning goals are directed at developing improving one’s skills and competence. Hence, learners pursuing learning goals should focus on the actual learning process, striving to improve their skills personally [31]. Against these plausible considerations, it is no surprise that learning goals are connected to active and deep learning [32]. In light of this and prior research [30,33], learning goals should be positively associated with learning engagement and learning gains. Furthermore, Daumiller and colleagues [33] found that the effect of achievement goals on learning gains was mediated by learning engagement.
On the other hand, work avoidance goals are directed at avoiding work and effort. Hence, learners pursuing work avoidance goals should strive to do as little as possible during an online course; they might even opt out. Active and deep learning are not at issue. That is, work avoidance goals should be negatively associated with learning engagement and learning gains. On a side note, other interesting goal classes refer to performance or relational goals [31]. Learners pursuing performance goals are focused on demonstrating competence rather than developing it (unlike learners pursuing learning goals). Learners with pronounced relational goals strive for relationships with other learners. However, as mentioned above, we are focusing solely on learning goals and work avoidance goals when addressing achievement goals in this paper.

3. Hypotheses

For the present paper, we evaluated a five-lesson synchronous online course on the topic Perceptional Psychology for police students. Our aim was to analyze the influence of interruptions and learner factors such as interest and achievement goals on learning engagement and outcomes.
For a simple yet effective way to evaluate a course’s effectiveness, the concept of self-efficacy comes in handy. Built and based on the work of Bandura [34], self-efficacy refers roughly to the learners’ confidence in their ability to perform a certain task successfully. Self-efficacy is known as a predictor of university grades (e.g., [35]) and performance (e.g., [36]). There is also a plethora of research about how to foster self-efficacy (e.g., [36,37]). Hence, it is feasible to assume that an effective digital learning environment will also foster its learners’ self-efficacy [38]. In the current paper, our synchronous online course should thus also increase the learners’ self-efficacy as a measure of course effectiveness. Therefore, for a start, we predicted:
Hypothesis 1: 
The online course would foster student’s self-efficacy.
From a more explorative perspective, we predicted:
Hypothesis 2a: 
Positive correlations between learning gains and posttest performance.
Hypothesis 2b: 
Negative correlations between age and posttest performance.
For our main hypotheses relying on the aforementioned background, we aimed to analyze the influence of learner factors such as interest and achievement goals on learning engagement and outcomes. We predicted:
Hypothesis 3a: 
Interest would reveal a positive effect on learning gains mediated by learning engagement.
Hypothesis 3b: 
Learning goals would have a positive effect on learning gains mediated by learning engagement.
Hypothesis 3c: 
Work avoidance would show a negative effect on learning gains mediated by learning engagement.
Finally, we predicted:
Hypothesis 4: 
Interruptions would negatively affect posttest performances.

4. Method

4.1. Sample and Procedure

Our sample comprised 136 police students at a German university of applied sciences (N = 136; 37 females, 96 males; Mage = 29.58 years, SD = 5.66). As we will address in the limitations section, our sample’s age reveals a rather large standard deviation. This reflects the two different access paths to the study of policing in Germany: after graduating from secondary school, which applies to ~25% of the students, or after years of practical duty as a police officer, which applies to ~75% of the students.
This study was conducted completely online during the winter semester of 2021/22. The participants attended in groups of maximum 20 students during a five-lesson online course about the topic Perceptional Psychology in the special field of police management. Those five lessons spanned one week and took place in a synchronous online format, conducted via MS Teams. The first author lectured the course and shared power point slides. He also implemented various didactic measures to ensure a high-quality distance course (see e.g., [39]), such as a preparatory countdown at the beginning, creating interactions, discussing examples, etc. The goal was to avoid a lecture monologue in front of quiet black boxes that reflect learners’ passivity, as discussed at this paper’s beginning. Rather, we considered the students’ learning engagement a key factor in the course’s success, following the aforementioned Active Processing stance [15,16] and the ICAP framework [17].
Before the online course started, the participants received data protection information. They also provided informed consent to participating in this voluntary and anonymous study. They were then given a questionnaire on learning goals, work avoidance goals, interest, self-efficacy, and demographics (i.e., age and sex). After the second and after the fourth (of five) lessons, participants received a questionnaire on learning engagement and number of interruptions. After the online course was finished, they received the final questionnaire on self-efficacy, learning gains, and the knowledge test.

4.2. Measures and Instruments

4.2.1. Learning Goals

To assess learning goals, we applied the scale by Daumiller and Dresel [40] and slightly customized the items to accommodate the police course at hand. All four items (e.g., “I want to constantly improve my competences.”) were answered on a Likert-type scale ranging from 1 (do not agree at all) to 8 (agree completely). We used the mean of all four items as measure of learning goals (Cronbach’s α = 0.88).

4.2.2. Work Avoidance Goals

Work avoidance goals were assessed with four items by Daumiller and Dresel [40]. Again, all four items (e.g., “I want to have as little to do as possible.”) were answered on a Likert-type scale ranging from 1 (do not agree at all) to 8 (agree completely). We used the mean of all four items as measure of work avoidance goals (Cronbach’s α = 0.93).

4.2.3. Interest

We assessed the participants’ interest with four items based on studies by Schiefele [25] on topic interest. The items’ stem was “I consider the topic ‘perception’ as…” followed be two items regarding the emotional component of interest (“…boring” and “…interesting”) and two items regarding the value component of interest (“…unimportant” and “…useful”). All four items were answered on a Likert-type scale ranging from 1 (disagree completely) to 4 (agree completely). We used the mean of all four items as a measure of interest (Cronbach’s α = 0.81).

4.2.4. Self-Efficacy

To assess self-efficacy focusing on our online course’s topic, we handed out the same questionnaire as pretest and posttest. We used the stem “Please mark how confident are you about the following…” followed by four items (e.g., “I know basic principles of perceptional psychology”). All four items were answered on a Likert-type scale ranging from 1 (lowest) to 5 (highest). We used the mean of all four items as measure of interest (Cronbach’s αPretest = 0.82 and Cronbach’s αPosttest = 0.84).

4.2.5. Learning Engagement

Learning engagement was assessed with six items after the second and fourth (of five) lessons. We relied on items used by Daumiller and colleagues [33], which they had selected from established scales [33,40,41,42,43,44]. An example item was “I made a lot of effort to understand everything”. We used the mean of all 12 items as measure of learning engagement (Cronbach’s α = 0.75).

4.2.6. Learning Gains

Learning gains were assessed with five items right after the online course ended. We relied on a subscale from the German translation of Marsh’s [45,46] Students’ Evaluations of Educational Quality (SEEQ) [33]. An example item was “I have learned a lot in this course”. Participants could mark their agreement on a Likert-type scale from 1 (completely disagree) to 8 (completely agree). We used the mean of all five items as measure of learning gains (Cronbach’s α = 0.85).

4.2.7. Number of Interruptions

The number of interruptions was assessed with a single item as in Hefter’s study [12]: “Were you interrupted by other people or events/incidents during this web-based lecture?” after the second and fourth lesson. Participants answered on 5-point scale from 0 (no interruption) to 4 (more than three interruptions) and we used the mean of both items.

4.2.8. Posttest Performance

Finally, we assessed the participants’ knowledge on perceptional psychology after the online course with 16 true–false items. For each true–false item, one point was awarded for the correct choice, resulting in a maximum of 16 points. We used the sum of all 16 items as a measure of posttest performance (Cronbach’s α = 0.59).

5. Results

This paper relies on the classic conventions: alpha-level of 0.5 for all tests, ηp2 as the effect size for F tests and Pearson’s correlation coefficient r for correlations. Value qualifications were: for ηp2, values below 0.06 as small, between 0.06 and 0.13 as medium, and above 0.13 as large effects. For r, values around 0.10 as small, around 0.30 as moderate, and above 0.50 as large correlations [47]. Table 1 reports means and standard deviations as well as intercorrelations for all measures.

5.1. Effects on Self-Efficacy

To test our hypothesis (Hypothesis 1) that the online courses would boost the learners’ self-efficacy, we conducted a one-way repeated-measure ANOVA with measurement time as a within-subjects factor, and self-efficacy as dependent variable. Comparing the pretest and posttest, it revealed a significant effect of measurement time, F(1, 78) = 112.68, p < 0.001, ηp2 = 0.59 (large effect).

5.2. Age and Interruptions Negatively Affect Test Performance

As our correlation analyses revealed, the self-reported learning gains correlated highly with actual test performance in the posttest (Hypothesis 2a), r = 0.34, p = 0.002, (moderate correlation). The only other correlations we observed were (as expected) with the number of interruptions, r = −0.24, p = 0.034, and age (Hypothesis 2b), r = −0.27, p = 0.017, (moderate correlation). For further analyses, we conducted a multiple linear regression model with test performance as the criterion variable and added predictors stepwise such as the number of interruptions (Hypothesis 4) and (from a rather explorative perspective) age. The first regression model using age as a predictor was statistically significant, F(1, 78) = 5.98, p = 0.017, R2adjusted = 0.060, with age as a statistically significant negative predictor, β = −0.27, p = 0.009 (one-sided). The second regression model, using age and the number of interruptions was also statistically significant, F(2, 78) = 5.44, p = 0.006, R2adjusted = 0.102, revealing an increased amount of explained variance, ΔR2 = 0.053. The number of interruptions was a statistically significant negative predictor too, β = −0.23, p = 0.018 (one-sided).
Interestingly (but not surprisingly), learning goals and work avoidance goals correlated statistically significantly with the number of interruptions. The higher the learning goals, the fewer the interruptions, r = −0.21, p = 0.032. The higher the work avoidance goals, the more interruptions, r = 0.22, p = 0.027.

5.3. Effects of Interest, Learning Goals, and Work Avoidance Goals on Learning Gains

We assumed that interest, learning goals, and work avoidance goals would demonstrate an effect on learning gains mediated by learning engagement (Hypotheses 3a, 3b, 3c). For our mediation analysis, we relied on the software JASP (Version 0.17.2.1) [48]. We entered interest, learning goals, and work avoidance goals as the predictors, learning engagement as the potential mediating variable, and learning gains as the dependent variable. The software calculated 95% bootstrap percentile CIs from 5000 bootstrap samples. It revealed the following effects (standardized estimates) on learning engagement, R2 = 0.40. We found statistically significant effects (a paths) of interest and work avoidance goals on learning engagement, aInterest = 0.62, p < 0.001 and aWork-Avoidance = −0.19, p = 0.003, but not of learning goals, aLearning = 0.06, p = 0.467. Learning engagement revealed a statistically significant effect on learning gains, b = 0.42, p < 0.001. There was also a direct effect of interest on learning gains c′Interest = 0.60, p < 0.001 and an indirect effect of interest on learning gains via learning engagement, aInterest × b = 0.26 [0.07, 0.60]. Furthermore, we detected an indirect effect of work avoidance goals on learning gains via learning engagement, aWork-Avoidance × b = −0.08 [−0.18, −0.02]. However, a statistically significant indirect effect of learning goals on learning gains via learning engagement could not be detected, aLearning × b = 0.03 [−0.04, 0.12]. The confidence intervals not including zero support the conclusion that learning engagement mediated both the positive effect of interest and the negative effect of work avoidance goals on learning gains. Figure 1 illustrates our mediation results.

6. Discussion

The present field study relied on an authentic synchronous distance learning setting and analyzed the influence of interest and achievement goals on learning engagement and outcomes. We thus contribute to theory and practice with ecological validity.

6.1. Theoretical Contributions and Practical Implications

First, our findings show that the synchronous online course was successful. It significantly fostered the students’ self-efficacy. Furthermore, their evaluations of their learning gains during the course were quite high (M = 6.08 on a scale from 1 to 8). These ratings are on a level resembling previous manifestations of the course when it was in person. For a start, our findings imply that a synchronous online course remains similarly effective and well liked when switched from an in-person to a distance learning setting. It should be kept in mind, however, that we had addressed various didactic measures ensuring a high-quality distance course that ensures learner engagement. After all, we considered the students’ learning engagement a key factor in the course’s success, following the aforementioned Active Processing stance [15,16] and the ICAP framework [17].
Furthermore, the students’ self-perceived learning gains correlated highly with their actual test performance after the course. We can therefore rule out a mismatch between subjective and objective evaluations, which should obviously be avoided [49]. Rather, this finding of ours highlights the validity of the self-report scales we applied [33]. We thus recommend those scales further for the practice of evaluating online courses in the field. One of the advantages of these self-report scales is that learners can answer them quickly and instructors get their results, for instance, on learning engagement or gains, in no time. More objective methods to assess learning engagement would involve more laborious methods, such as having the learners write down notes and then having experts rate the notes’ quality (e.g., [6]).
We also detected a negative association between age and test performance. This might be explained by the characteristic sample comprising both secondary school graduates and experienced police officers, and we address it in the limitations section below.
Our main results are that we have gained deeper insight into the connections between learning prerequisites, such as interest and achievement goals with learning engagement and outcomes. Interest and both achievement goals (i.e., learning goals and work avoidance goals) did not intercorrelate. These results underline those constructs’ different foci and contexts. The interest variable we assessed in the present study referred to a certain type of interest, namely topic interest [22]. This topic interest directly referred to the course content (i.e., perceptional psychology). In contrast, learning and work avoidance goals were rather domain-general achievement goals [33]. Our mediation analyses revealed interest to be a predictor of learning gains, mediated by learning engagement. In other words, the more interested learners are in the course topic, the more numerous their learning gains after the course. This effect was mediated by learning engagement. Furthermore, work avoidance goals, shown to be a negative predictor of learning gains, were also (negatively) mediated by learning engagement. While work avoidance goals’ contribution to the mediation model fell behind that of topic interest, it still played a statistically significant role. As expected, work avoidance goals proved to be negatively associated with learning engagement and learning gains. This result is in line with previous research [33]. Consequently, focusing on minimizing effort is maladaptive for learning engagement and gains. The contribution of learning goals, however, failed to reach statistical significance—unlike in the study by Daumiller and colleagues [33]. One plausible reason for this might be our sample’s selection effects. As participation was voluntary, learners with rather low learning goals might have dropped out. All in all, the mediating role of learning engagement underlines the importance of having students actively engage during the course.
From a practical perspective, it would seem worthwhile for instructors to ensure that learners do not pursue work avoidance goals, but rather learning goals during a synchronous online course. For instance, instructors could inform their learners about the benefits of learning goals and emphasize how important it is to actively engage in the course.
Finally, we detected a negative association between learning outcomes and interruptions during the course. Whereas previous studies identified this effect in asynchronous distance learning scenarios (e.g., [6,12]), we also detected it in the present synchronous setting. The number of (self-reported) interruptions was a negative predictor for performance in the posttest. From a practical perspective, it is thus advisable for instructors to make their students aware of the detrimental effects of interruptions on learning outcomes, especially during an online course.

6.2. Limitations

As a limitation, our study focused on a certain course for police students. The topic perceptional psychology was appropriate for a synchronous online format, unlike for instance, the topics of conflict management or operational training, which obviously require an in-person setting. Our findings’ generalizability might be limited across other domains, such as mathematics, but also across other student samples. Whereas optimizing university courses for police students is probably an under-researched topic, our police students differ from regular students in two ways: first, course attendance is mandatory because it is part of their employment status. Participating in our study was voluntary, though. Second, there are two access paths to the study of police work: the first path is directly after passing the higher education entrance exam after graduating from secondary school. This applies to ~25% of the students. These students are more or less in their early 20s. The other access path is after years of actual duty as a police officer. This applies to ~75% of the students. These students are roughly in their 30s. Whereas the first group may lack practical experience but is more accustomed to theoretical learning and knowledge tests (hence their performance advantage in this study), the second group possesses a treasure trove of practical experience, but their years of schooling are farther back in the past. During the courses, both groups study together, benefiting from each other’s interactive exchange of experiences.
Finally, our course was a five-lesson intervention with an immediate posttest afterwards. Future studies should analyze the influence of topic interest and achievement goals on learning engagement and gains with longer interventions and delayed posttests for potential long-term effects.

7. Conclusions

Despite these limitations, the present findings concisely provide ecologically valid empirical insights in a successful synchronous online course. Our results imply that instructors should consider learner characteristics, such as topic interest and achievement goals as important learning predictors. Furthermore, the great importance of actively engaging in the course should be emphasized. Finally, the detrimental effects of interruptions on learning outcomes should be considered. Future research might focus on potential long-term effects.

Author Contributions

Conceptualization, M.H.H.; methodology, M.H.H.; formal analysis, M.H.H.; investigation, M.H.H.; resources, H.N.; data curation, M.H.H.; writing—original draft preparation, M.H.H.; writing—review and editing, M.H.H. and H.N.; visualization, M.H.H.; project administration, H.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and the Ethical Guidelines of the German Association of Psychologists (DGPs). It was and approved by the Ethics Committee of Bielefeld University (No. 2021–267).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available from the corresponding author upon reasonable request.

Acknowledgments

We thank all students who took part in our study and Carole Cürten for proofreading.

Conflicts of Interest

The authors declare no conflicts of interest.

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Education 14 00118 g001
Figure 1. Statistically significant mediation results.
Figure 1. Statistically significant mediation results.
Education 14 00118 g001
Table 1. Descriptive statistics and correlations for all measures.
Table 1. Descriptive statistics and correlations for all measures.
Descriptive StatisticsManifest Correlations
MSD12345678
(1) Learning goals 16.841.26
(2) Work avoidance goals 13.421.60−0.24
(3) Interest 23.420.530.15−0.05
(4) Pretest self-efficacy 32.930.81−0.140.010.27
(5) Posttest self-efficacy 33.830.600.17−0.200.430.34
(6) Learning engagement 14.570.840.19−0.300.340.130.33
(7) Learning gains 16.081.100.18−0.120.460.100.520.51
(8) Interruptions 41.771.32−0.210.22−0.030.08−0.08−0.11−0.22
Posttest performance 510.333.52−0.02−0.210.120.040.300.050.34−0.24
Note. 1 Scale from 1 (lowest) to 8 (highest), 2 Scale from 1 (lowest) to 4 (highest), 3 Scale from 1 (lowest) to 5 (highest), 4 Scale from 0 (no interruption) to 4 (more than three interruptions), 5 Number of correct answers, max 16, bold correlations: p < 0.05 (two-sided).
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Hefter, M.H.; Nitsch, H. Synchronous Distance Learning: Effects of Interest and Achievement Goals on Police Students’ Learning Engagement and Outcomes. Educ. Sci. 2024, 14, 118. https://0-doi-org.brum.beds.ac.uk/10.3390/educsci14020118

AMA Style

Hefter MH, Nitsch H. Synchronous Distance Learning: Effects of Interest and Achievement Goals on Police Students’ Learning Engagement and Outcomes. Education Sciences. 2024; 14(2):118. https://0-doi-org.brum.beds.ac.uk/10.3390/educsci14020118

Chicago/Turabian Style

Hefter, Markus H., and Holger Nitsch. 2024. "Synchronous Distance Learning: Effects of Interest and Achievement Goals on Police Students’ Learning Engagement and Outcomes" Education Sciences 14, no. 2: 118. https://0-doi-org.brum.beds.ac.uk/10.3390/educsci14020118

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