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Article

Emotional Experience and Depth of Reflection: Teacher Education Students’ Analyses of Functional and Dysfunctional Video Scenarios

by
Anne Schlosser
* and
Jennifer Paetsch
Faculty of Education Science, University of Bamberg, 96047 Bamberg, Germany
*
Author to whom correspondence should be addressed.
Educ. Sci. 2025, 15(8), 1070; https://doi.org/10.3390/educsci15081070
Submission received: 11 July 2025 / Revised: 15 August 2025 / Accepted: 18 August 2025 / Published: 20 August 2025
(This article belongs to the Special Issue The Role of Reflection in Teaching and Learning)
Review Reports Versions Notes

Abstract

An important objective of teacher education is to encourage students to reflect on teaching practices. Analyzing video scenarios from classroom settings is a commonly used method for achieving this. This study examines the impact of different video types on the reflective and emotional processes of teacher education students and explores the relationships between these processes. In a randomized experimental study, 129 students analyzed a video of either a dysfunctional or a functional video scenario as part of a video-based intervention. Data were collected through written reflections, self-assessments of reflection, and ratings of emotional valence and arousal. The results revealed that students who analyzed the dysfunctional scenario demonstrated greater levels of reflection and experienced more negative emotions than those who analyzed the functional scenario. No significant differences were found in terms of self-assessed reflection and positive emotions. However, a significant relationship was found between positive emotions and self-assessed reflection. This study contributes to the literature by differentiating between distinct facets of reflection and emotion, thus enabling a more nuanced understanding of how specific video characteristics influence reflective engagement.

1. Introduction

A key goal of teacher education is to encourage prospective teachers to reflect on teaching practices to enhance their professional competencies (e.g., Baumert & Kunter, 2013; Collin et al., 2013; Weber et al., 2023). Reflective practice is often facilitated by providing students with support tools for reflection, such as text- or video-based examples of teaching practice (Collin et al., 2013; Seidel et al., 2011). Numerous studies have demonstrated the effectiveness of classroom videos in deepening reflection among pre- and in-service teachers (e.g., Weber et al., 2023; Weber et al., 2018; Kleinknecht & Poschinski, 2014). Notably, this effect is not limited to authentic recordings of real-world teaching but also applies to staged videos portraying exemplary teaching scenarios (Thiel et al., 2020). Staged video scenarios typically present teaching situations in a more focused and less complex way than authentic classroom recordings (Blomberg et al., 2013) and are therefore often employed in teacher education. Staged videos increase the likelihood that students will recognize relevant teaching situations, particularly given that prior research has shown that they often struggle to identify such moments (e.g., Blomberg et al., 2013; Gaudin & Chaliés, 2015). Thiel et al. (2020) analyzed staged videos, including functional video scenarios (FVS) that showed positive examples and dysfunctional video scenarios (DVS) that demonstrated negative examples of classroom situations. They observed that both types of video scenarios can enhance knowledge-acquisition, self-efficacy, and reflection (Thiel et al., 2020).
Classroom videos can trigger emotional responses that influence learning processes and outcomes in (multimedia) learning contexts (e.g., Knörzer et al., 2016; Pekrun, 2006; Kleinknecht & Poschinski, 2014; Weber et al., 2023). The impact of video scenarios on students’ emotions can be explained using appraisal theory (e.g., Lazarus & Lazarus, 1994) and control-value theory of achievement emotions (Pekrun, 2006). These theories propose that emotions arise when an individual cognitively evaluates a situation with respect to its personal relevance and their perceived ability to cope with it.
A few studies have investigated the emotional experiences of students when analyzing authentic videos (e.g., Kleinknecht & Poschinski, 2014; Schlosser & Paetsch, 2023a; Weber et al., 2023). However, there is a notable lack of systematic, quantitative investigations into the emotional experiences of teacher education students when analyzing staged videos. To our knowledge, no previous research has explored how the analysis of functional versus dysfunctional classroom scenarios evokes specific emotional responses and how these emotions, in turn, shape the cognitive processes of reflection.
This study aimed to address this gap by investigating the emotional processes and depth of reflection of teacher education students when analyzing functional video scenario (FVS) and dysfunctional video scenario (DVS) with a focus on classroom management in elementary school teaching. We employed a randomized experimental design, which was pre-registered (cf., Schlosser & Paetsch, 2023b) prior to the study’s completion. First, the study focused on the effects of FVS and DVS on depth of reflection and emotional experience—that is, emotional valence and arousal. Second, we investigated the relationship between emotional experience and depth of reflection. Third, we examined the relationship between experiences observing teaching and learning and teacher education students’ emotional responses. We employed a multi-method approach, incorporating self-assessed measures of reflection with an objective indicator based on an analysis of students’ written reflections, enabling a more nuanced understanding of the impacts of analyzing staged videos on teacher education students.

2. Theoretical Framework

2.1. Functional and Dysfunctional Video Scenarios in Teacher Education

An advantage of staged videos is that they facilitate the targeted recording of classroom scenes, in accordance with the learning objectives of initial teacher education. Classroom situations can therefore be exemplified as either typical, particularly successful (“functional”), or negative (“dysfunctional”). While FVS demonstrates successful teacher behavior as evidenced by education research and encourages model learning, DVS illustrates undesirable teacher behavior, adopting a more problem-oriented learning approach (e.g., Wilkes et al., 2022; Piwowar et al., 2017; Thiel et al., 2020). Recognizing dysfunctional behavior in these videos can lead to a reevaluation of one’s ideas about effective teaching, prompting a deeper analysis of the causes underlying a critical situation and fostering the development of alternative strategies (cf. Thiel et al., 2020).
Thiel et al.’s (2020) study investigated the effectiveness of FVS and DVS. Participants engaged in a video-based learning arrangement, during which they received theoretical input and analyzed either FVS or DVS, both individually and collaboratively. Results revealed benefits for both groups in terms of professional vision (including noticing, reasoning, and generating alternatives), professional knowledge, and self-efficacy. Furthermore, analyzing the FVS was associated with greater improvements in professional knowledge, whereas analyzing the DVS more effectively fostered reasoning skills (Thiel et al., 2020).

2.2. Video-Based Reflection in Teacher Education

Reflection as a cognitive process plays a decisive role in the development and enhancement of teachers’ professional competencies (cf. Elsner et al., 2020). The term “reflection” is frequently used in teacher education, but there is a lack of precise explication and differentiation from related concepts, such as professional vision (e.g., Lenske & Lohse-Bossenz, 2023). The present article adopts the definition proposed by Lenske and Lohse-Bossenz (2023), which is based on the works of Schön (1983), Aeppli and Lötscher (2016), Clarà (2015), and Hatton and Smith (1995), for example. Within this framework, reflection refers to the cognitive process that follows the observation of an event. Context-related cognitive processing, explicit self-reference, and expanding the understanding of pedagogical practice are core elements of the reflection process. These cognitive processes are characterized by traceability and should occur in light of theoretical or personal experience. Several sub-processes, such as describing and interpreting, are proposed (Lenske & Lohse-Bossenz, 2023). The explicit self-reference entails a critical reflection on one’s own professionalization, which may relate, for example, to one’s own attitude, emotions during the event, or one’s ability to associate the event with existing theories (Lenske & Lohse-Bossenz, 2023; Aeppli & Lötscher, 2016). Ultimately, reflection aims to deepen the understanding of pedagogical practice, which should, in turn, foster changes at the level of professional action (e.g., generating alternatives; Lenske & Lohse-Bossenz, 2023).
In the context of video-based reflection, the distinction between reflection and professional vision is not always clear-cut (e.g., Lenske & Lohse-Bossenz, 2023; Weber et al., 2023), as both constructs focus on cognitive processes. For example, reasoning is a component of professional vision, which involves interpreting a situation based on theoretical knowledge (e.g., Gamoran Sherin & van Es, 2009). This cognitive activity creates conceptual proximity to reflection, which also involves interpretation. However, unlike reflection, professional vision focuses on the perception of the situation (noticing), which is not a component of reflection. Furthermore, explicit self-reference is not part of the construct of professional vision (e.g., König et al., 2022; Lenske & Lohse-Bossenz, 2023).
A precise definition of the concept of reflection depth remains to be established (e.g., Lenske & Lohse-Bossenz, 2023; Arendt et al., 2025). However, stage models address depth of reflection, which assume a progressively increasing level of cognitive engagement. According to Hatton and Smith (1995), critical reflection represents the highest level of reflection (e.g., critique of assumptions, context, and ethical implications), while Kleinknecht and Gröschner (2016) stress that the generation of alternative actions is the most advanced level. More recent discussions on the construct of reflection depth suggest that it arises from engaging with various reflection categories (e.g., describing, interpreting, generating alternatives) and integrates additional components such as knowledge, attitudes, and goals (cf. Aeppli & Lötscher, 2016). Common across these perspectives is the understanding that reflection depth entails increasing conceptual elaboration (e.g., describing, interpreting) that is grounded in theoretical understanding and a critical engagement with the generation of action alternatives.
Previous empirical studies have evaluated reflection using written reflections (e.g., Kücholl & Lazarides, 2021; Richter et al., 2022). Kücholl and Lazarides (2021) and Richter et al. (2022) focused on the reflection components of describing, interpreting, and generating alternative strategies. Kücholl and Lazarides (2021) found no significant differences in the reflection on one’s own versus others’ videos among teacher education students. Similarly, Richter et al. (2022) found no significant differences between an authentic classroom video group and a virtual reality (VR) video group in terms of their written reflections. Explicitly addressing the self-referential component of reflection, Schlosser and Paetsch (2023a) used self-assessment instruments to measure participants’ reflections on their theoretical evaluations and contextualizations (cf. Reinders, 2016). Results indicated that the perceived ability to reflect theoretically was higher in the group that reflected on authentic classroom videos with structured observation tasks than in a text-based control group and a group that reflected on authentic classroom videos with open observation tasks. Learning processes and outcomes have been investigated in the context of staged videos (Wilkes et al., 2022), as well as professional vision (Thiel et al., 2020). However, written reflection in this context remains unexplored.
The present study is built upon the outlined conceptualization of reflection. Thus, the video-based intervention specifically targets the cognitive subprocesses of describing and interpreting, as well as expanding pedagogical understanding by generating alternative courses of action.

2.3. Emotional Responses in Video-Based Reflection

Classroom video analyses can elicit emotions that can be characterized as achievement emotions, which are defined as “emotions tied directly to achievement activities or achievement outcomes” (Pekrun, 2006, p. 317). This definition of emotion distinguishes between emotions that prospectively or retrospectively address the outcomes of performance events and activity-related emotions that pertain to learning activities. Emotions can be described across two dimensions, with arousal ranging from high to low and valence ranging from positive to negative (Russell & Barrett, 1999; Tellegen et al., 1999). Appraisal theory posits that the valence of an emotion is influenced by a process of subjectively evaluating (appraising) a stimulus (e.g., Lazarus & Lazarus, 1994). Emotional arousal is associated with psychophysiological activation of the autonomic nervous system in response to a stimulus (e.g., Kreibig & Gendolla, 2014; Wuensch et al., 2025). Emotional arousal is also influenced by the individual’s involvement in, or the personal significance they ascribe to, the situation presented (cf. Lazarus & Lazarus, 1994). Emotions can be assessed either categorically (e.g., joy, anger; Positive and Negative Affect Schedule (PANAS); cf. Breyer & Bluemke, 2016) or dimensionally (high or low valence and arousal; cf. affect grid; Russell et al., 1989) using subjective measures. A suitable objective method for assessing emotional arousal involves recording electrodermal activity via skin electrodes to capture related physiological responses, such as changes in sweat production (Wuensch et al., 2025).
Emotions elicited during video observation depend not only on the properties of the video stimulus itself (cf. Scherer, 2005) but also on how the individual cognitively appraises the presented situation (e.g., Lazarus & Lazarus, 1994). The personal relevance of the situation, among other factors, shapes individuals’ appraisal processes. In the teacher education context, this relevance may stem from the professional significance of classroom videos and their perceived usefulness, as they present authentic or exemplary scenarios related to the learners’ anticipated future work environment (e.g., Seidel et al., 2011; Tucholka et al., 2025; Zhang et al., 2011). In addition, the degree of immersion—understood as the subjective feeling of being involved in or present within the situation (cf. Goldman, 2007)—can further influence the level of perceived personal involvement when watching classroom videos (e.g., Kleinknecht & Poschinski, 2014; Seidel et al., 2011). This sense of involvement enhances the perceived immediacy and relevance of the situation and may therefore intensify emotional responses, affecting both the appraisal process and emotional experience (e.g., Lazarus & Lazarus, 1994).
Staged videos in the teacher education context are typically less complex and present more explicit scenes compared to videos of authentic classroom scenarios, which may make it easier for beginner students to identify and evaluate relevant moments (e.g., Seidel et al., 2011). In line with appraisal theory (e.g., Lazarus & Lazarus, 1994), this increased clarity may enhance the situation’s perceived significance, potentially eliciting stronger emotional reactions (e.g., arousal) compared to authentic classroom videos. Based on the situational specificity of emotions (e.g., Scherer, 2005), it can be assumed that functional video scenarios are more likely to evoke positive emotions, while dysfunctional video scenarios may elicit more negative emotional responses.
Some studies have examined emotional experiences in the context of classroom video analyses. Syring et al. (2015) showed that when teacher education students analyzed authentic classroom videos, they experienced greater joy and immersion compared to a text-based control group (Syring et al., 2015). In their qualitative study with teacher education students, Chan et al. (2018) found that analyzing one’s own videos, in particular, evokes negative emotions, whereas analyzing videos of others is associated with positive emotional experiences. Egloff and Souvignier (2020) showed that when watching classroom videos, teacher education students experience greater emotional arousal and more positive emotions compared to students analyzing expert talks. These lines of research suggest that emotions are elicited by analyzing classroom videos, but the emotional valence differs depending on the type of video (cf. Chan et al., 2018; Egloff & Souvignier, 2020; Syring et al., 2015). To what extent the use of staged videos evokes differences in emotional responses among teacher education students remains an open question.

2.4. Relationship Between Emotion and Reflection

Ambivalent assumptions can be derived from theories regarding the influence of emotions on cognition (cf. Stark, 2016; Frenzel et al., 2009; Weber et al., 2023). Specifically, two differing hypotheses emerge in this context, reflecting divergent views on how emotions impact cognitive functioning. The “emotion-as-suppressor” hypothesis proposes that emotions have an inhibitory effect on learning due to the engagement of cognitive resources (e.g., Um et al., 2012; Stark, 2016; Frenzel et al., 2009). In contrast, the “emotions-as–facilitators of learning” hypothesis suggests that positive emotions enhance the learning process by influencing processing mechanisms (e.g., Stark, 2016; Um et al., 2012; Frenzel et al., 2009; Fredrickson, 2001).
Several studies have investigated the correlation between emotional experience and depth of reflection when analyzing classroom videos. Kleinknecht and Poschinski’s (2014) study employed a case analysis involving experienced teachers and determined that emotional experience is associated with the depth of reflection. The analysis of negative scenes, in particular, increased the experience of negative emotions and was associated with a greater depth of reflection (Kleinknecht & Poschinski, 2014). Schlosser and Paetsch (2023a) investigated teacher education students’ positive and negative emotional arousal while they analyzed authentic classroom videos, finding no significant differences in emotional experiences between two intervention groups (with or without differentiated observation tasks) and a text-based control group. However, they did find a significant improvement in positive emotional arousal across all groups. Their study also revealed a significant correlation between positive emotions—but not negative emotions—and reflection (cf. Schlosser & Paetsch, 2023a). The results of both studies (cf., Kleinknecht & Poschinski, 2014; Schlosser & Paetsch, 2023a) are consistent with the assumption that emotions and cognition are related (cf., Stark, 2016; Frenzel et al., 2009; Pekrun, 2006; Weber et al., 2023). The correlation between positive emotions and reflection (cf. Schlosser & Paetsch, 2023a) also aligns with the emotion-as-facilitators of learning hypothesis (cf., Stark, 2016; Um et al., 2012; Frenzel et al., 2009; Fredrickson, 2001).

2.5. The Current Study

Despite the increasing use of videos in teacher education, no systematic analysis has been conducted to examine the effects of different classroom video scenarios on reflection or the emotions of teacher education students (e.g., Blomberg et al., 2013). To address this gap in existing research, we investigated the impact of different video scenarios (dysfunctional and functional) on reflection and emotion and the relationships between these processes.
Recent research has shown that analyzing DVS is more effective in improving reasoning level than analyzing FVS (Thiel et al., 2020). The question remains whether the results can also be applied to the depth of reflection. Drawing upon this, we tested the following hypothesis:
Hypothesis 1 (H1). 
Participants who analyze DVS exhibit a greater depth of reflection than participants who analyze FVS.
To the best of our knowledge, only a few studies have investigated emotional experiences elicited among teacher education students during the analysis of classroom videos (Egloff & Souvignier, 2020; Schlosser & Paetsch, 2023a; Syring et al., 2015). However, they have produced conflicting results due to their varying designs and diverse samples (e.g., Kleinknecht & Poschinski, 2014; Chan et al., 2018; Egloff & Souvignier, 2020; Schlosser & Paetsch, 2023a). Based on previous studies showing that classroom video analyses can evoke (positive) emotions (e.g., Syring et al., 2015; Chan et al., 2018; Egloff & Souvignier, 2020; Schlosser & Paetsch, 2023a; Tucholka et al., 2025) and the assumption that emotions are situation-dependent (cf. Lazarus & Lazarus, 1994), we also tested the following hypotheses:
Hypothesis 2 (H2). 
Analyzing FVS (a) and analyzing DVS (b) will increase the positive valence of students’ emotions.
Hypothesis 3 (H3). 
Students analyzing DVS will experience more negative emotions compared to students analyzing FVS.
Hypothesis 4 (H4). 
(a) Students in both groups (analyzing FVS, analyzing DVS) will report greater emotional arousal after video analysis compared to before. (b) Students who analyze DVS will display more emotional arousal after video analysis than those who analyze FVS.
Building upon the findings of Schlosser and Paetsch (2023a), who observed an increase in positive emotional arousal but no differences in negative emotional arousal during the analysis of authentic classroom videos, it is prudent to consider additional factors that influence emotional responses in relevant learning experiences. Based on emotion theories, both involvement (see immersion; e.g., Kleinknecht & Poschinski, 2014; Weber et al., 2023) and personal significance are relevant for the emergence of emotional arousal (cf. Lazarus & Lazarus, 1994). Personal significance, in particular, can arise when an individual has already had relevant prior experiences and when the current learning experience is perceived as personally relevant and useful (e.g., Egloff & Souvignier, 2020; Lazarus & Lazarus, 1994; Schlosser & Paetsch, 2023a). We therefore tested the following hypothesis:
Hypothesis 5 (H5). 
(a) Experiences of observing teaching, (b) perceived usefulness, (c) perceived importance, and (d) high immersion will lead to greater emotional arousal in both groups.
A further research desideratum emerges from the need to comprehensively explore the complex relationship between emotions and reflection in the context of analyzing classroom videos. Theoretical (for a summary, see Stark, 2016) and empirical evidence (cf. Kleinknecht & Poschinski, 2014; Schlosser & Paetsch, 2023a) suggest that emotion and reflection are related. Therefore, we tested the following hypothesis:
Hypothesis 6 (H6). 
Negative emotions, positive emotions, and emotional arousal correlate with depth of reflection.

3. Materials and Methods

3.1. Intervention and Procedure

The current study is part of a larger research project. We pre-registered the research project before data collection (see Schlosser & Paetsch, 2023b). Deviating from the pre-registration, the title of the present paper was clarified, and the order of the hypotheses was adjusted. The intervention targeted teacher education students enrolled in elementary teacher education training courses at University 1. Recruitment was later expanded to a second university (University 2) to achieve the calculated sample size.
We adopted a pre–post experimental study design to measure changes during the intervention. Data were collected during a self-learning intervention between January and July 2023. Students took part in the study (1) as part of an asynchronous lecture session, (2) as part of a face-to-face seminar session, or (3) outside of a course in the laboratory. Participants used the same online learning platform to standardize implementation. Participants were randomly assigned to two different groups. The type of classroom video was experimentally varied: in Group Functional (FVS), the students analyzed an FVS, while in Group Dysfunctional (DVS), students analyzed a DVS of the same teaching situation. All participants received the same reflection tasks and the same questionnaires. The intervention procedure was otherwise held constant in both groups. All participants provided informed consent.
Participants worked individually on their own devices during the study. Each student initially received a link to the theoretical introduction to classroom management (self-developed, based on the Linzer Concept of Classroom Management; cf., Lenske & Mayr, 2015) via an online learning platform (Moodle). The pre-test took place after the theoretical introduction and before the video analysis (via Moodle), which the students completed individually while guided by observation tasks (see Supplementary Table S1). Participants in Group FVS analyzed one FVS, and participants in Group DVS analyzed one DVS. The staged videos lasted 3–4 min and were sourced from the CLIPPS video portal of the University of Duisburg Essen. The selected videos were designed based on the Linzer Concept of Classroom Management (cf. Lenske & Mayr, 2015), with each showing more functional (https://www.uni-due.de/clipss/video.php?videoid=5, accessed on 9 December 2022) or more dysfunctional (https://www.uni-due.de/clipss/video.php?videoid=6, accessed on 9 December 2022) teacher behavior in response to the same teaching scenario (Bönte et al., 2019). The videos show two parallel cases of starting a lesson, and both start in the same way (the teacher enters the classroom). In one case, the teacher applies functional classroom management strategies (e.g., clarifying the rules of behavior using rituals like a singing bowl, presenting the daily structure by pupils), and the pupils react accordingly. In the dysfunctional video scenario, the teacher acts in a dysfunctional manner (e.g., no established rituals, unclear instructions). This means that the pupils do not respond to the teacher’s signals, the lesson’s start is less structured, and it takes significantly longer than in the functional video (for detailed information about the production and the videos, see Piwowar et al., 2017; Lenske et al., 2022). For example, participants in the study may reflect that although the teacher in the DVS uses a singing bowl, she gives unclear instructions and has to admonish the students individually to go to their seats. Immediately after analyzing the video, participants completed the observation tasks and the post-test.

3.2. Participants

Participating teacher education students were enrolled from two German universities (University 1 and University 2). Our recruitment target was 140–200 participants, based on power analyses previously conducted. For the pre-registration, it was stated that only students from one lecture on primary school pedagogy would participate in the study. However, additional students from University 1 and some students from University 2 were recruited to achieve the target sample size. Email was used to advertise the study to potential participants. In total, 129 students participated in the intervention (71.3% female), with 49 (84% female) taking part in the study as part of an asynchronous lecture session, 41 (48% female) as part of a face-to-face seminar session, and 48 (82% female) in the laboratory. Sixty-one (n = 42 University 1; n = 19 University 2) teacher education students participated in Group FVS, and 68 (n = 47 University 1; n = 21 University 2) in Group DVS. Overall, the participants had an average age of 20.93 (SD = 3.10) years. Among them, 112 (86.8%) were beginner students in their first or second semester. In Group FVS, 85.2% of the participants were female, 85.2% were beginner students, and the average age was 21.3 years (SD = 3.56). In Group DVS, 75.0% of the participants were female, 88.2% were beginner students, and the average age was 20.6 years (SD = 2.55).

3.3. Instruments

3.3.1. Depth of Reflection

In line with the idea that various methods capture distinct aspects of reflection, we implemented two different approaches: self-assessed reflection for self-reference and written reflections for evaluating cognitive processes (e.g., describing, interpreting, generating alternatives) and expanding the understanding of pedagogical practice (Lenske & Lohse-Bossenz, 2023). To evaluate self-assessed reflection, we employed two scales from Reinders (2016) for the post-test (see Supplementary Table S2). The scales measured theoretical evaluation with four items (e.g., “Theories help me to better understand educational situations that I have experienced”) and theoretical contextualization with five items (e.g., “I can immediately think of a theory that would have predicted the outcome of the situation’”). Participants rated their answers on a 4-point Likert scale. The reliability of these scales was good (αevaluation = 0.86; αcontextualization = 0.72).
We used written reflections to capture cognitive processes, thereby providing an additional objective measure of reflection. We developed the observation tasks according to the theoretical assumptions of Lenske and Lohse-Bossenz (2023) and empirical studies by Kleinknecht and Poschinski (2014), Richter et al. (2022), and Kücholl and Lazarides (2021). We developed six open-ended observation tasks to reflect on teacher behavior (see Supplementary Table S1). The focus here was on the cognitive processes of describing, interpreting, and generating alternative strategies. For example, participants were asked to describe a critical teaching situation in the video and reflect on it in the context of theoretical concepts. Finally, they were asked to consider the consequences of teaching behavior and generate alternative teaching strategies. Researchers evaluated students’ answers using a coding scheme (see Supplementary Table S1). The development of the coding scheme was guided by aspects of reflection explicitly outlined in existing research (cf. Elsner et al., 2020; Kleinknecht & Poschinski, 2014; Richter et al., 2022; Kücholl & Lazarides, 2021). The assessments were based on whether the participants provided coherent and comprehensible descriptions, interpretations, and generation of alternatives, as well as whether they made precise references to theoretical models. Participants could achieve a total of 13 points. Two independent raters each coded 50% of the responses, demonstrating good agreement (inter-rater reliability = 0.8).

3.3.2. Emotional Responses

Participants’ emotional responses were assessed using a self-report measure before and after the video analysis. We used an affect grid (Russell et al., 1989) to measure emotional arousal on a one-dimensional scale. An affect grid (Russell et al., 1989) allows participants to plot both the arousal and valence of their emotional experiences on a 10-point scale in a coordinate system, providing single-item scales for emotional arousal and emotional valence. To measure the levels of positive and negative emotional valence in more detail, we used the Positive and Negative Affect Schedule (PANAS) (Breyer & Bluemke, 2016) (see Supplementary Table S2). The PANAS (Breyer & Bluemke, 2016) captures emotional valence with 20 emotion adjectives (rated on a 5-point Likert scale; e.g., “active,” “scared,” “enthusiastic”), which can be grouped into two scales (10 items each) for positive and negative emotional valence. Both scales showed good reliability for both the pre-test (αpositive = 0.84; αnegative = 0.80) and the post-test (αpositive = 0.86; αnegative = 0.83). During the pre-test, instructions focused on the participants’ emotional state at the current moment, while the post-test instructions related to the analysis of the video scenario (e.g., “How do you feel at the moment?”; “How did you feel during the video analysis?”).

3.3.3. Experience in Observing Teaching and Perceived Learning Experience

As part of the pre-test, we assessed prior experience in observing teaching using a self-developed item (“Do you have any experience in observing teaching?”). Participants rated their experience on a 5-point scale. Furthermore, participants were asked in the post-test to rate their perceived learning experience of classroom video analysis. Perceived usefulness was measured using four items adapted from Teo (2019) (α = 0.70), scored on a 5-point Likert scale. An example of one such item is: “My classroom management skills benefit from classroom video analysis.” The experience of immersion was captured in the post-test by asking students to rate their experience using one item adapted from Syring et al. (2015), scored on a 4-point Likert scale (“I felt like I had been in the classroom”). The perceived importance of classroom management competencies was assessed using a scale adapted from Hertel’s (2009) work, comprising seven items related to the subjective importance of classroom management (e.g., “It is personally important for me to learn classroom management skills”), scored on a 7-point Likert scale. However, the reliability of this scale was found to be insufficient (α = 0.63), so it was excluded from the subsequent analysis.

3.4. Statistical Analysis

Before the intervention was carried out, statistical power analyses were performed using the G*Power 3.1 software program. Our goal was to obtain 0.95 power to detect a medium effect size of 0.25 at the standard 0.05 alpha error probability. Based on the G*Power analyses, our target sample was 70 participants per intervention group (140 total). SPSS version 28 (IBM Corporation, Armonk, NY, USA) was used for statistical analysis of the data. Deviating from the pre-registration, we conducted outlier analyses on the dependent variables to enhance data quality due to observed pre-test differences. Initially, univariate outliers were examined using box plots. Consistent with recommendations in the literature, we applied the criterion of plus or minus three median absolute deviations (Leys et al., 2019; Field, 2013), which led to the identification of nine outliers. Regarding the outliers, no systematic differences were observed in the demographic variables. However, eight of the outliers were situated in Experimental Group FVS. We presumed these to be random outliers and excluded them from subsequent analyses. The examination of multivariate outliers revealed no further outliers (Leys et al., 2019). Data were missing completely at random based on Little’s (1988) MCAR test (χ2 = 54.98, df = 38, p = 0.175). To handle the small number of missing data, listwise deletion was used in all analyses. A MANOVA was conducted to test Hypothesis 1, because the depth of reflection was operationalized through various dependent variables. Hypotheses 2, 3, and 4 were tested using two-factor ANOVAs with repeated measures. Regression analyses were calculated to assess Hypotheses 5 and 6.

4. Results

4.1. Descriptive Results

Descriptive statistics (see Table 1) revealed pre-test differences in negative emotions. Group FVS exhibited higher levels of negative emotions, which decreased significantly following the intervention. The level of negative emotions in Group DVS remained similar throughout the intervention. There were no significant changes in either group regarding positive emotions. Finally, emotional arousal increased in Group DVS post-intervention but remained almost the same in Group FVS.
The assessment of reflection was only conducted in the post-test. Descriptives (see Table 1) show that greater mean values were observed for theoretical evaluation compared to theoretical contextualization. For both scales, self-assessed reflection was higher in the post-test. The written reflections also yielded high values (max. = 13 points), with significantly higher values in Group DVS. Table 2 shows all bivariate correlations.

4.2. Group Differences in Depth of Reflection

Hypothesis 1 assumes that students who analyze DVS show a greater depth of reflection than students who analyze FVS. The analysis revealed statistically significant group differences (one-way MANOVA: F(3, 124) = 6.71, p < 0.001, η2 = 0.14). Bonferroni-corrected post hoc tests showed that students in Group DVS exhibited significantly higher scores in the written reflections than those analyzing the FVS (F(1, 124) = 20.36, p < 0.001, η2 = 0.14). Regarding self-assessed reflection for theoretical evaluation (F(1, 124) = 0.13, p = 0.72) and theoretical contextualization (F(1, 124) = 0.50, p = 0.48), we found no significant differences between the two groups. As such, Hypothesis 1 can be accepted for the written reflections but not for self-assessed reflection.

4.3. Group Differences in Emotional Experience

Hypothesis 2 postulated that video analysis would increase positive emotions in both groups. A two-factor ANOVA with repeated measures revealed no significant main effect (F(1, 124) = 1.13, p = 0.29) and no significant interaction effect (F(1, 124) = 0.00, p = 0.98) regarding positive emotions. As a result, Hypothesis 2 was rejected.
Assessing changes in negative emotions using two-factor ANOVA with repeated measures revealed a significant main effect (F(1, 124) = 9.56, p = 0.00, η2 = 0.10) and a significant interaction effect (F(1, 124) = 6.50, p = 0.01, η2 = 0.05). Consequently, Hypothesis 3 was accepted. Students who analyzed the DVS experienced negative emotions at the same level, while the level of negative emotions decreased for those who analyzed the FVS.
It was assumed that emotional arousal would increase in both groups (hypothesis 4a) and students who analyze DVS will display more emotional arousal than those who analyze FVS. Results of the two-factor ANOVA with repeated measures showed no significant effects for time (F(1, 122) = 2.66, p = 0.11) or the grouping factor (F(1, 122) = 2.41, p = 0.12). As a result, Hypotheses 4a and 4b must be rejected.

4.4. Effects of Experience in Observing Teaching and Perceived Experiences During Video Analysis on Emotional Arousal

The investigation of predictors of emotional arousal (significant regression model: F(5, 118) = 2.98, p = 0.014) during video analysis showed that emotional arousal (pre) and immersion were significant predictors of emotional arousal. However, perceived usefulness and experiences observing teaching were not significant predictors (see Table 3). Thus, Hypothesis 5 can be partially accepted, only for immersion.

4.5. Relationship Between Emotional Experience and Depth of Reflection

Finally, the relationship between emotion and depth of reflection was examined using regression analyses (see Table 4). Results indicated that, when controlling for group affiliation, emotional arousal and emotional valence are not significant predictors of written reflections (F(4, 119) = 6.46, p < 0.001). However, group affiliation was revealed to be a significant predictor. Regarding theoretical evaluation (F(4, 119) = 2.84, p = 0.027) and theoretical contextualization (F(4, 119) = 3.89, p = 0.005), positive emotions proved to be significant predictors. Thus, Hypothesis 6 can be partially accepted.

5. Discussion

The present study focused on the impact of analyzing staged videos of classroom scenarios in elementary school teaching on the emotional and reflective processes of teacher education students. The experimental pre–post study design included two intervention groups in which teacher education students analyzed either an FVS or a DVS of the same teaching situation.

5.1. Depth of Reflection

One strength of our study is that we used multiple approaches to measure reflection, acknowledging that common definitions of the term highlight its multifaceted nature (e.g., Lenske & Lohse-Bossenz, 2023; Arendt et al., 2025; Aeppli & Lötscher, 2016). While written reflection captures aspects of cognitive processes, self-assessed reflection also encompasses self-reference (e.g., Arendt et al., 2025; Clarà, 2015; Lenske & Lohse-Bossenz, 2023; Aeppli & Lötscher, 2016).
Our results indicate that analyzing a DVS is associated with a deeper level of reflection in written reflections. Previous studies found no significant differences between authentic classroom videos and VR videos (Richter et al., 2022) or between one’s own and others’ videos (Kücholl & Lazarides, 2021). Our results indicate that the type of video scenario influences reflective cognitive processes. This result aligns with that of Thiel et al. (2020), who found that students who analyzed DVS achieved higher reasoning scores (one aspect of professional vision) than those who analyzed FVS. One possible explanation for this could be that beginner students often experience difficulties in perceiving critical events (e.g., Piwowar et al., 2017; Blomberg et al., 2013), so it is conceivable that DVS appear easier to perceive due to irritation (see the theory of problem-oriented learning; e.g., Thiel et al., 2020). Another explanation could lie in the assessment of written reflections. Students who analyze a DVS observe dysfunctional teacher actions, for which alternative actions could be generated more easily than for functional teacher actions.
Contrary to our assumption, we found no significant differences between Groups FVS and DVS in terms of self-assessed reflection. One possible explanation may be that the specific video content does not strongly shape self-referential reflection, which may reduce the likelihood of observable differences resulting from variations in video type compared to more content-sensitive reflection processes (e.g., Lenske & Lohse-Bossenz, 2023; Aeppli & Lötscher, 2016). Based on our results, it may be inferred that, with regard to self-referential reflection, it does not appear to matter whether functional or dysfunctional aspects of teacher behavior in classroom management are considered.
However, there was also no significant correlation between written reflection and self-assessed reflection. This can be interpreted in the context of self-reference and cognitive processes, which represent distinct constructs. While cognitive processes are content-related, self-reference focuses on one’s own experiences (cf. Lenske & Lohse-Bossenz, 2023; Aeppli & Lötscher, 2016).
Overall, the mean score of written and self-assessed reflections was above the average in both groups, indicating that participants performed the observation tasks in accordance with the instructions and were, thus, able to reflect classroom management in elementary school teaching in a guided manner. It can therefore be assumed that both the FVS and DVS analyses, guided by observation tasks, offered a suitable occasion for reflection (e.g., Elsner et al., 2020; Thiel et al., 2020; Richter et al., 2022; Kücholl & Lazarides, 2021).

5.2. Emotions

To facilitate a more comprehensive and nuanced understanding of the emotional responses involved, we captured both self-assessed emotional arousal and emotional valence.
Results showed that positive emotions (i.e., valence) were relatively constant across the groups and measurement times. However, the intervention did not elicit significant differences in positive emotions, and no significant interaction effects emerged, indicating no increase in positive emotions in either group during their analysis of the videos. This result contradicts the assumption that analyzing positive classroom scenes triggers more positive emotional experiences (e.g., Lazarus & Lazarus, 1994; Scherer, 2005). Furthermore, this result conflicts with empirical findings of the pre–post-test study of Syring et al. (2015), which showed that analyzing authentic classroom videos elicits positive emotions. The explanation for these various results could lie in the differences in study design. In their work, Syring et al. (2015) compared an authentic video group with a text-based control group, focusing solely on distinctive emotions (e.g., joy, anger) and embedding the intervention in a seminar concept led by lecturers. Thus, unlike in our intervention, the emotional experience in Syring et al.’s (2015) study cannot be attributed solely to the video analysis, as other factors—such as the guidance provided during the intervention and the varying methods of analysis (group work versus individual work)—could have influenced the emotional experience. Another possible explanation for the constant level of positive emotions during our intervention is that beginner students do not perceive particularly successful scenes regarding classroom management in the FVS as such (e.g., Piwowar et al., 2017; Blomberg et al., 2013) and, consequently, do not report changes in positive emotional experiences.
Additionally, variance analysis showed a significant main effect and a significant interaction effect regarding negative emotions. For students who analyzed the FVS, negative emotional valence decreased significantly, while it remained almost the same for those who analyzed the DVS. These findings are consistent with those of other studies, showing that differences in videos depicting classroom scenes could result in different effects on negative emotions. For example, Chan et al. (2018) demonstrated that analyzing one’s own video elicits more negative emotions than analyzing the videos of others. On the one hand, our results can be interpreted within the context of appraisal theory, as a scene in an FVS that is perceived as positive tends to reduce negative emotions (Lazarus & Lazarus, 1994; Scherer, 2005). On the other hand, it remains unclear why negative emotions do not increase when analyzing dysfunctional scenes. Perhaps students employ coping mechanisms (e.g., reframing) that mitigate the increase in negative emotions or distance themselves cognitively, for example, by focusing on the analysis (Lazarus & Lazarus, 1994). Results regarding negative emotions should be interpreted with caution, as there were significant pre-test differences in our study. These differences could be explained in the context of the high individual variability in the emotional as well as individual and situational experiences that occurred immediately before the intervention (e.g., Lazarus & Lazarus, 1994; Russell et al., 1989).
We also focused on changes in emotional arousal. Our results showed no significant changes in emotional arousal during the intervention in either group. These findings are inconsistent with those of Egloff and Souvignier (2020), who observed greater emotional arousal among teacher education students who watched authentic classroom videos compared to those who watched an expert talk. However, these conflicting results can be interpreted in light of the divergent video material. While Egloff and Souvignier (2020) showed several video scenarios with comments and interviews from students and teachers, our study focused on analyzing a short video depicting a classroom scenario. Participants in our study therefore had to recognize and evaluate critical scenes themselves without receiving comments and evaluations from others. As already mentioned, this is particularly challenging for beginner students (e.g., Piwowar et al., 2017; Blomberg et al., 2013).
With another hypothesis, we examined whether prior experiences or aspects of perceived learning experience predict emotional arousal (post-test) after video analysis. Emotional arousal prior to the video analysis (pre-test) and immersion significantly predict emotional arousal (post-test). Group affiliation, experience observing teaching, and perceived usefulness did not predict emotional arousal. The degree to which students engaged deeply with the video scenario affected emotional arousal, which underlies theoretical assumptions (e.g., Lazarus & Lazarus, 1994; Goldman, 2007) and previous empirical investigations (e.g., Kleinknecht & Poschinski, 2014; Seidel et al., 2011). Even if prior experiences of observing teaching and perceived usefulness are highly pronounced, these do not contribute to explaining the emotional arousal. These results contradict our theoretical assumption (e.g., Lazarus & Lazarus, 1994). This may be because of the complex nature of the construct of emotional arousal, which is influenced by a range of situational, interpersonal, and contextual factors (e.g., Lazarus & Lazarus, 1994; Scherer, 2005; Wuensch et al., 2025). The measurement instruments may also provide an explanation. For example, emotional arousal was measured subjectively; an objective assessment of emotions might lead to different results (e.g., Wuensch et al., 2025). It is also questionable to what extent emotional arousal was reported solely by analyzing the video scenarios, as contextual factors, task settings, and other factors can also influence the results (e.g., Lazarus & Lazarus, 1994). Further research should consider the extent to which the emotional experience could be recorded in a more isolated manner when analyzing lesson videos.
Overall, our results indicated that analyzing staged video scenarios is not as emotionally stimulating as expected.

5.3. Correlation of Depth of Reflection and Emotions

We investigated the association between emotion and reflection in the context of classroom video analysis. Emotional arousal, positive emotions, and negative emotions did not significantly predict written reflection quality. Our results contradict those of Kleinknecht and Poschinski (2014), who found a connection between depth of reflection and negative emotions in a qualitative study with experienced teachers. The different samples could potentially explain these findings. While experienced teachers participated in Kleinknecht and Poschinski’s (2014) intervention, our sample primarily consisted of beginner students. In addition, Kleinknecht and Poschinski (2014) used open questions to elicit reflection and emotion during the video analysis, using content analysis to evaluate their responses. In contrast, our results rely on written reflections and the self-assessment of emotions, where there may already be limited correlation due to the different data collection methods. Thus, the results can be interpreted in light of the low correlations between objective and self-assessed data (e.g., Baumert & Kunter, 2013).
Regarding self-assessed reflection, positive emotions proved to be a significant predictor, indicating that they were associated with self-assessed reflection. This is in line with the emotions-as-facilitators of learning hypothesis (e.g., Stark, 2016; Um et al., 2012), which posits that positive emotions are associated with a greater assessment of one’s own depth of reflection. Furthermore, this result aligns with findings from a previous study by Schlosser and Paetsch (2023a), which showed a correlation between self-assessed reflection and positive emotions in the context of analyzing videos of authentic classroom scenarios.
In summary, the relationship between emotion and depth of reflection depends upon which facet of reflection is being examined. Previous studies have also found correlations between emotion and depth of reflection when using self-assessments or qualitative content analysis (e.g., Kleinknecht & Poschinski, 2014; Schlosser & Paetsch, 2023a); however, there is no evidence regarding the relationship between written reflection and emotion. Future studies should explore a broader range of measurement techniques to determine how different methods might affect the observed relationship between emotion and reflection.

5.4. Limitations and Further Research

The interpretation of our results must take into account the specific limitations of our study. First, we were unable to achieve the ideal sample size, which had been calculated in advance using power analyses. Possibly, results with medium effects that did not reach the level of significance in the current study would have shown significant effects with a larger sample size. Specifically, this limits the interpretation of the relationship between emotion and reflection. A further limitation is the existence of pre-test differences, despite randomization and outlier analysis, regarding negative emotions. These differences can be explained by the high individuality of emotions (e.g., Lazarus & Lazarus, 1994; Russell et al., 1989; Tellegen et al., 1999), limiting the interpretation of changes in negative emotions. Due to our study design, no control group was employed, which is why we cannot determine the effectiveness of the intervention on the depth of reflection. To enable statements about the effectiveness of video-based interventions on the depth of reflection, subsequent studies should also include a control group (e.g., completing text-based tasks). Furthermore, assessing written reflections during the pre-test is also necessary to draw conclusions about the effectiveness of the intervention. Another limitation is that emotions were only measured using self-assessment instruments, which rely on participants’ subjective perceptions and may not accurately reflect their actual emotional experience (e.g., Wuensch et al., 2025). This could have impacted the validity of the measurements. In addition, our results show that there is no correlation between self-assessed and written reflection, and they demonstrate different effects, which is why the different facets of reflection should also be taken into account in future studies. A further limitation of the present study lies in its restricted generalizability, as emotional responses were examined based on the analysis of only a single classroom video. Future research could replicate the effects of staged video scenarios on emotions using consecutive analyses of video scenarios.
Our results indicate that both types of videos provide valuable opportunities for reflection, despite being grounded in different learning mechanisms—analyzing DVS promotes learning from errors, while analyzing FVS supports model learning. Therefore, combining both types of videos seems promising for future studies. However, the optimal sequence for fostering reflection remains unclear, as does the impact of video sequencing on emotional experience. It may also be beneficial for future studies to include different learning settings, such as individual video or collaborative video analyses.

6. Conclusions and Implications for Teacher Education

Our findings support the notion that analyzing staged video scenarios is a valuable tool for students of teacher education, providing meaningful opportunities for reflection on classroom management in elementary school teaching. Analyzing teacher behavior in FVS and DVS through guided observation tasks appears to foster a deeper level of reflection. The practical implication is that analyzing DVS and FVS supports reflection in terms of self-reference, whereas analyzing DVS may support cognitive processes of reflection, such as describing, interpreting, and generating alternatives. Our research also contributed to a better understanding of whether and how emotional responses arise during the analysis of staged video scenarios. There were changes in negative affect depending on the video type, suggesting that the analysis of staged videos elicits emotional responses. We have not investigated whether the results are generalizable to authentic classroom videos or video scenarios in other subjects or school types; however, it can be assumed that critically perceived scenes also have an influence on emotional reactions and depth of reflection. Finally, we demonstrated that the experience of positive emotions is positively correlated with self-assessed reflection, but not with written reflections, suggesting different learning mechanisms. A practical implication of this is that university educators may consider selecting the type of video scenario depending on the learning objectives of their intervention. For example, learning with FVS corresponds to model learning, whereby positive emotions contribute to an increase in reflective self-reference. Although our findings indicate that using staged video scenarios offers meaningful opportunities for reflection in teacher education, further research is needed to explore how to support reflection through a systematic consideration of emotional stimulation.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/educsci15081070/s1, Table S1: Observation tasks and coding scheme for written reflections (English translation); Table S2: Evaluation tools (English translation).

Author Contributions

Conceptualization, A.S. and J.P.; methodology, A.S. and J.P.; software, A.S.; validation, A.S. and J.P.; formal analysis, A.S.; investigation, A.S. and J.P.; resources, A.S. and J.P.; data curation, A.S.; writing—original draft preparation, A.S.; writing—review and editing, J.P.; visualization, A.S.; supervision, A.S.; project administration, J.P.; funding acquisition, J.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by “Stiftung Innovation in der Hochschullehre” as part of the “Digitale Kulturen der Lehre entwickeln (DiKuLe)” project. The publication of this article was supported by the Open Access publication fund of the University of Bamberg.

Institutional Review Board Statement

Ethical review and approval were waived for this study by the Ethics Committee of the University of Bamberg, Germany. According to a unanimous decision, the committee confirms that ethical approval was not required in this case.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
DVSDysfunctional Video Scenario
FVSFunctional Video Scenario
Group DVSGroup Dysfunctional Video Scenario
Group FVSGroup Functional Video Scenario

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Table 1. Descriptive results.
Table 1. Descriptive results.
Group FVSGroup DVS
Pre–Post-Test Differences Pre–Post-Test DifferencesPre-Test Differences
MSDNMissingT(df)pMSDNMissingT(df)pT(df)p
Emotional valence aPre6.102.02610−1.07 (59)0.286.561.936621.09(63)0.28−1.32 (125)0.10
Post6.352.16016.251.74653
Emotional arousal aPre5.432.09610−0.06 (59)0.965.171.86662−2.23(63)0.03−0.74 (125)0.46
Post5.401.816015.881.76653
Positive emotions bPre2.960.64610−0.82 (59)0.422.900.62671−0.71(65)0.480.53 (126)0.59
Post3.000.736012.960.68671
Negative emotions bPre1.680.516104.57 (59)<0.0011.460.476710.35(65)0.732.49 (126)0.01
Post1.390.496011.440.49671
Theoretical evaluation cPost3.740.74610 3.830.80671
Theoretical contextualization cPost3.160.70610 3.210.58671
Written reflection dPost8.591.98610 9.971.43671
Experience observing teaching bPre3.031.26610 2.750.99671 1.69 (126)0.10
Immersion bPost3.221.25610 3.131.30671
Perceived usefulness bPost4.120.22601 4.310.59671
Note. a 10-point scale. b 5-point scale. c 4-point scale. d max. = 13 points.
Table 2. Bivariate correlations.
Table 2. Bivariate correlations.
Group FVS
(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)
Emotional valence (pre)
Emotional valence (post)0.50 **
Emotional arousal (pre)0.030.08
Emotional arousal (post)0.040.110.32 *
Positive emotions (pre)0.27 *0.38 **0.240.13
Positive emotions (post)0.150.39 **0.26 *0.200.71 **
Negative emotions (pre)−0.36−0.31 *−0.10−0.10−0.08−0.31 *
Negative emotions (post)−0.26−0.53 **−0.04−0.00−0.06−0.150.54 **
Theoretical evaluation (post)−0.020.090.180.28 *0.190.30 *−0.210.03
Theoretical contextualization (post)−0.120.03−0.02−0.030.37 **0.41 **−0.070.020.32 *
Written reflection (post)−0.20−0.040.250.26 *0.060.23−0.190.010.180.06
Experience observing teaching (pre)0.050.050.29 *0.130.100.18−0.20−0.19−0.040.080.22
Immersion (post)0.120.040.230.050.30 *0.33 *−0.120.090.26 *0.090.33 *0.12
Perceived usefulness (post)0.080.11−0.050.240.120.16−0.11−0.070.120.210.130.020.21
Group DVS
Emotional valence (pre)
Emotional valence (post)0.23
Emotional arousal (pre)−0.10−0.12
Emotional arousal (post)−0.23−0.140.12
Positive emotions (pre)0.31 *0.130.170.01
Positive emotions (post)0.090.41 *−0.060.170.57 **
Negative emotions (pre)−0.51 **−0.020.210.08−0.120.09
Negative emotions (post)−0.33 **−0.45 **0.030.31 *−0.18−0.29 *0.15
Theoretical evaluation (post)−0.050.170.04−0.030.180.28 *−0.05−0.03
Theoretical contextualization (post)0.110.16−0.120.35 **0.100.23−0.22−0.090.29 *
Written reflection (post)0.110.21−0.050.05−0.060.030.04−0.030.08−0.05
Experience observing teaching−0.11−0.05−0.10−0.100.160.150.04−0.18−0.060.01−0.01
Immersion (post)−0.20−0.160.31 *0.40 **0.200.190.230.31 *0.180.020.000.07
Perceived usefulness (post)−0.150.01−0.24 *−0.08−0.04−0.000.080.030.16−0.010.11−0.12−0.21
Note. Regression coefficients * p < 0.05, ** p < 0.01.
Table 3. Regression analysis for emotional arousal (post-test).
Table 3. Regression analysis for emotional arousal (post-test).
βp
Group0.140.13
Emotional arousal (pre)0.180.05
Experience observing teaching−0.010.89
Perceived usefulness0.100.25
Immersion0.200.03
R2 = 0.11, corrected R2 = 0.08
Note. β = standardized regression coefficient. NGroup FVS = 60, NGroup DVS = 64.
Table 4. Regression analysis for reflection (post-test).
Table 4. Regression analysis for reflection (post-test).
Written ReflectionTheoretical EvaluationTheoretical Contextualization
βpβpβp
Group0.36<0.0010.090.330.030.71
Arousal0.140.120.070.440.090.31
Positive emotions0.120.190.260.010.31<0.001
Negative emotions−0.020.850.010.890.020.83
R2 = 0.18,
corrected R2 = 0.15		R2 = 0.08,
corrected R2 = 0.05		R2 = 0.09,
corrected R2 = 0.06
Note. β = standardized regression coefficient. NGroup FVS = 60, NGroup DVS = 64.
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Schlosser, A.; Paetsch, J. Emotional Experience and Depth of Reflection: Teacher Education Students’ Analyses of Functional and Dysfunctional Video Scenarios. Educ. Sci. 2025, 15, 1070. https://doi.org/10.3390/educsci15081070

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Schlosser A, Paetsch J. Emotional Experience and Depth of Reflection: Teacher Education Students’ Analyses of Functional and Dysfunctional Video Scenarios. Education Sciences. 2025; 15(8):1070. https://doi.org/10.3390/educsci15081070

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Schlosser, Anne, and Jennifer Paetsch. 2025. "Emotional Experience and Depth of Reflection: Teacher Education Students’ Analyses of Functional and Dysfunctional Video Scenarios" Education Sciences 15, no. 8: 1070. https://doi.org/10.3390/educsci15081070

APA Style

Schlosser, A., & Paetsch, J. (2025). Emotional Experience and Depth of Reflection: Teacher Education Students’ Analyses of Functional and Dysfunctional Video Scenarios. Education Sciences, 15(8), 1070. https://doi.org/10.3390/educsci15081070

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