Introduction to Vol. 13 No. 4 (2025)
2026-06-19
Dear Reader,
I am happy to announce the publication of a new issue of Frontline Learning Research (the fourth of 2025).
This issue holds five articles that examine learning processes and educational experiences, between them combining investigations of innovative research methods and technologies (multimodal data, VR, and eye tracking), studies of learner behavior and engagement in educational contexts and learners’ conceptualizations of educational scientists.
Niehorster et al. started from the premise that classrooms are becoming increasingly digital. One novel digital technology that might enter classrooms soon is eye tracking. This is a method to estimate a person’s gaze direction and infer the point of visual attention. This technology has already provided insights into how students process computer-based instructional material. Moreover, it has been used to enhance pre-recorded video instruction, by showing learners where the teacher is looking, which made it easier for learners to follow and learn from the teacher (i.e., eye movement modeling examples). Thus far, this was only implemented in pre-recorded videos. In their paper, they present a proof-of-concept, with a focus on the technical set-up, that shows how this approach can also be used in real time, during teaching. They recorded the eye movements of a teacher giving a PowerPoint presentation and displayed his eye-movements in real-time to students who were present in the same classroom. Students saw one of three different versions of the presentation: The PowerPoint only (1), the PowerPoint with an overlay of the teacher’s eye movements as a circle (2) or as an inversed blurring of the material (3). Eye movement displays significantly affected how closely students followed the teacher’s gaze, but had no impact on learning. They also discuss open research questions and possible future applications in educational practice.
Riegel et al. started from the premise that there are empirical studies on both the cognitive and performative outcomes of learning via VR and the affective aspects of the use of VR in learning. Studies on affective outcomes in relation to the learning object, such as the attitude towards it or the atmospheric effect of a visited environment on the students, are still pending. They addressed this desideratum of educational research by comparing the auratic experience elicited by physical church visits with that elicited by virtual church visits via Head Monted Displays (HDMs). Therefore, the study applied an experimental design with two points of measurement and two treatments. Auratic experience was operationalized as both mystical experience and flow, while flow was structured according to the two dimensions of control and concentration. The participants were students of religious education (N = 52). In both virtual and physical exploration, there is hardly any mystical experience to be measured. However, it is the physical visit that stimulates a more pronounced experience than the virtual visit. However, the auratic experience in the given experiment is sensitive to the time of measurement: switching from virtual to physical exploration seems to stimulate an increase in the mystical experience, while switching from physical to virtual exploration somehow attenuates this experience. In their contribution they discuss this result and the limitations of the study.
In their paper, Nachtigall et al. address a critical and underexplored area in learning research: how learners conceptualize scientists beyond the natural sciences. While previous studies have extensively examined learners' perceptions of natural scientists, little is known about their understanding of social and educational scientists – fields often overlooked as scientific domains and not associated with competence. Given the potential influence of such conceptions on learners’ academic engagement, study choices, and career aspirations, their interview study provides an in-depth exploration of learners’ conceptions of the work and personal characteristics of educational scientists. The results of a qualitative content analysis show that the predominant image of the educational scientist among learners refers to a pedagogical practitioner who needs interpersonal skills to help children and families in educational and social service institutions. An additional typological content analysis reveals that learners who associate educational scientists with scientific activities and tasks tend to have uncertain, diffuse, and inaccurate ideas about the actual nature of this scientific work, putting into question whether this work can really be called research and imagining that educational scientists just sit together and think. Their findings also suggest that such misconceptions are rooted in learners’ school experiences. These findings point to a significant gap in the understanding of educational scientists and its potential consequences for engagement with the social sciences. This study emphasizes the need for targeted interventions to foster accurate conceptions about educational scientists, which ultimately could enhance learners’ appreciation of the social sciences and support more informed academic and career choices.
In their contribution, Eldemellawy et al. investigate the relationship between self-regulated learning (SRL) strategies and early dropout in a largely underexplored professional online learning context. Forty-two professionals (N = 42) enrolled in a four-week course delivered via a workplace Learning Experience Platform (LXP) supporting self-directed, adaptive learning pathways. SRL was measured using self-reports and behavioural trace data collected during the first week. A dual-method analytic approach was employed: top-down models (logistic regression and decision trees) tested predefined SRL predictors, while bottom-up methods (hierarchical clustering and process mining) uncovered emergent learner profiles without prior assumptions. Trace-based SRL indicators, particularly the number of unique actions on the platform, time spent during week one, participation in the forum, and completion of an action plan, were significantly stronger predictors of dropout (p < .05) than self-reports, where only the goal-setting subscale was significant. Despite the small sample size limiting generalisability, the study demonstrates that early behavioural engagement patterns can reliably predict learner persistence, often outperforming self-reported SRL measures. These findings advance our understanding of SRL in professional online learning environments and provide a replicable analytic framework for integrating trace data and surveys. By identifying actionable SRL behaviours linked to course completion, this study contributes toward the development of adaptive workplace learning systems capable of detecting and mitigating early disengagement in real time.
Finally, Michel et al. started from the premise that multimodal data analysis and validation based on streams from state-of-the-art sensor technology, such as eye-tracking or emotion recognition using the Facial Action Coding System (FACS) with deep learning, allows educational researchers to study multifaceted learning and problem-solving processes and to improve educational experiences. Their study aims to investigate the correlation between two continuous sensor streams—pupil diameter as an indicator of cognitive workload and FACS with deep learning as an indicator of emotional arousal (RQ1a)—specifically for epochs of high, medium, and low arousal (RQ1b). Furthermore, the time lag between emotional arousal and pupil diameter data will be analysed (RQ2). A total of 28 participants worked on three cognitively demanding and emotionally engaging everyday moral dilemmas while eye-tracking and emotion recognition data were collected. The data were pre-processed in Phyton (synchronization, blink control, and downsampling) and analysed using correlation analysis and Granger causality tests. The results show negative and statistically significant correlations between the data streams for emotional arousal and pupil diameter. However, the correlation is negative and significant only for epochs of high arousal, while positive but nonsignificant relationships were found for epochs of medium or low arousal. The average time lag for the relationship between arousal and pupil diameter was 2.8 ms. In contrast to previous findings without a multimodal approach suggesting a positive correlation between the constructs, the results contribute to the state of research by highlighting the importance of multimodal data validation and convergent validity. Future research should consider emotional regulation strategies and emotional valence.
Kind regards,
Professor, Dr. Nina Bonderup Dohn
Editor-in-Chief, Frontline Learning Research