Learners often experience aware and unaware mind wandering during educational tasks, both negatively impacting learning outcomes. Differentiating these types of task-unrelated thoughts is crucial, as they stem from different cognitive processes and warrant tailored support that addresses the specific nature of mind wandering. Automated detection of these episodes could help mitigate their adverse effects, for example, by developing adaptive, attention-aware learning environments. In this study (N = 87), we explored a novel multimodal approach, combining eye tracking, facial videos, and physiological wristbands (i.e., electrodermal activity and heart rate), to predict aware and unaware mind wandering during lecture video watching. In addition, to allow comparison to previous research, we also predicted an integrated mind-wandering category. Mind wandering was assessed using 15 two-stage thought probes to determine task-unrelated thoughts and the participants’ awareness of their mind wandering. Our findings indicate that a multimodal approach outperforms unimodal methods, utilizing the top 100 features from the fused data. Specifically, aware mind wandering was detected at 20% above chance (AUC-PR = 0.396), unaware mind wandering at 14% above chance (AUC-PR = 0.267), and the combined category at 40% above chance (AUC-PR = 0.637). Eye tracking and video features proved more predictive than physiological measures when used as standalone modalities. SHAP analysis, employed to explain the results, highlighted the significance of integrating features from all three modalities for effective detection, particularly emphasizing the role of video-based facial expressions in identifying unaware mind wandering. Going beyond the current state of the art, this study demonstrates the potential of leveraging multimodal data to enhance the precision of aware and unaware mind-wandering detection and differentiation, setting a foundation for advancing educational technologies that respond dynamically to learners’ cognitive states.

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.

Student engagement is a key component of learning and teaching, resulting in a plethora of automated methods to measure it. Whereas most of the literature explores student engagement analysis using computer-based learning often in the lab, we focus on using classroom instruction in authentic learning environments. We collected audiovisual recordings of secondary school classes over a one and a half month period, acquired continuous engagement labeling per student (N=15) in repeated sessions, and explored computer vision methods to classify engagement from facial videos. We learned deep embeddings for attentional and affective features by training Attention-Net for head pose estimation and Affect-Net for facial expression recognition using previously-collected large-scale datasets. We used these representations to train engagement classifiers on our data, in individual and multiple channel settings, considering temporal dependencies. The best performing engagement classifiers achieved student-independent AUCs of .620 and .720 for grades 8 and 12, respectively, with attention-based features outperforming affective features. Score-level fusion either improved the engagement classifiers or was on par with the best performing modality. We also investigated the effect of personalization and found that only 60 seconds of person-specific data, selected by margin uncertainty of the base classifier, yielded an average AUC improvement of .084.