Professional vision—the use of knowledge to notice and reason about classroom events—is an important facet of teacher expertise that is challenging for pre-service teachers (PSTs) to develop. Although feedback on written reflections of videotaped classroom events can improve professional vision, delivering feedback at scale requires significant time and labor. We explored the use of large language models (LLMs) to classify professional vision aspects in PSTs’ responses and provide feedback. We first conducted a technical evaluation to compare the classification performance of two prompting approaches (single-prompt and prompt-chaining). We then integrated the better-performing approach into a feedback prototype and conducted a user study with 13 PSTs. We analyzed PSTs’ interactions with the LLM-generated feedback. Overall, prompt-chaining showed higher inter-rater agreement with human coders in classifying professional vision than the single-prompt approach. PSTs who cycled between reading the feedback and revising reflection text showed more professional vision in the revisions, compared to those who spent more time passively reviewing feedback. We discuss the feasibility and limitations of LLM feedback in PST education and highlight considerations about AI feedback uptake.

This paper explores gaze entropy as a metric for detecting classroom discourse events in a virtual reality (VR) classroom. Using data from a laboratory experiment with N = 240 secondary school students, we distinguished between events of teacher-centered classroom discourse (question, hand raising, answer) and teacher explanation by analyzing their transition and stationary gaze entropy. Employing multi-level regression models, both entropy measures effectively discriminated between the two events and distinguished different levels of classroom participation as indicated by the degree of hand-raising by virtual students. Furthermore, using both measures in a logistic regression model, the potential of gaze entropy could be demonstrated by predicting the two events with 67% accuracy. By analyzing transition and stationary entropy, the study attempts to uncover different gaze patterns associated with learning events in a virtual classroom. The results contribute to the research and development of VR scenarios that help to simulate effective learning environments.

Currently, VR technology is increasingly being used in applications to enable immersive yet controlled research settings. One such area of research is expertise assessment, where novel technological approaches to collecting process data, specifically eye tracking, in combination with explainable models, can provide insights into assessing and training novices, as well as fostering expertise development. We present a machine learning approach to predict teacher expertise by leveraging data from an off-the-shelf VR device collected in a VirATec study. By fusing eye-tracking and controller-tracking data, teachers’ recognition and handling of disruptive events in the classroom are taken into account or considered. Three classification models were compared, including SVM, Random Forest, and LightGBM, with Random Forest achieving the best ROC-AUC score of 0.768 in predicting teacher expertise. The SHAP approach to model interpretation revealed informative features (e.g., fixations on identified disruptive students) for distinguishing teacher expertise. Our study serves as a pioneering effort in assessing teacher expertise using eye tracking within an interactive virtual setting, paving the way for future research and advancements in the field.

Virtual humans presented in VR learning environments have been suggested in previous research to increase immersion and further positively influence learning outcomes. However, how virtual human animations affect students’ real-time behavior during VR learning has not yet been investigated. This work examines the effects of social animations (i.e., hand raising of virtual peer learners) on students’ cognitive response and visual attention behavior during immersion in a VR classroom based on eye movement analysis. Our results show that animated peers that are designed to enhance immersion and provide companionship and social information elicit different responses in students (i.e., cognitive, visual attention, and visual search responses), as reflected in various eye movement metrics such as pupil diameter, fixations, saccades, and dwell times. Furthermore, our results show that the effects of animations on students differ significantly between conditions (20%, 35%, 65%, and 80% of virtual peer learners raising their hands). Our research provides a methodological foundation for investigating the effects of avatar animations on users, further suggesting that such effects should be considered by developers when implementing animated virtual humans in VR. Our findings have important implications for future works on the design of more effective, immersive, and authentic VR environments.

With rapid developments in consumer-level head-mounted displays and computer graphics, immersive VR has the potential to take online and remote learning closer to real-world settings. However, the effects of such digital transformations on learners, particularly for VR, have not been evaluated in depth. This work investigates the interaction-related effects of sitting positions of learners, visualization styles of peer-learners and teachers, and hand-raising behaviors of virtual peer-learners on learners in an immersive VR classroom, using eye tracking data. Our results indicate that learners sitting in the back of the virtual classroom may have difficulties extracting information. Additionally, we find indications that learners engage with lectures more efficiently if virtual avatars are visualized with realistic styles. Lastly, we find different eye movement behaviors towards different performance levels of virtual peer-learners, which should be investigated further. Our findings present an important baseline for design decisions for VR classrooms.

Recent developments in computer graphics and hardware technology enable easy access to virtual reality headsets along with integrated eye trackers, leading to mass usage of such devices. The immersive experience provided by virtual reality and the possibility to control environmental factors in virtual setups may soon help to create realistic digital alternatives to conventional classrooms. The importance of such settings has become especially evident during the COVID-19 pandemic, forcing many schools and universities to provide the digital teaching. Researchers foresee that such transformations will continue in the future with virtual worlds becoming an integral part of education. Until now, however, students' behaviors in immersive virtual environments have not been investigated in depth. In this work, we study students' attention by exploiting object-of-interests using eye tracking in different classroom manipulations. More specifically, we varied sitting positions of students, visualization styles of virtual avatars, and hand-raising percentages of peer-learners. Our empirical evidence shows that such manipulations play an important role in students' attention towards virtual peer-learners, instructors, and lecture material. This research may contribute to understanding of how visual attention relates to social dynamics in the virtual classroom, including significant considerations for the design of virtual learning spaces.