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Virtual reality (VR) makes learning more interesting for students and could help them remember what they have learned better than traditional methods. However, a student could get distracted in a VR environment because of stress, wandering thoughts, unwanted noise, outside sounds, etc. Distractions could be classified as either external (due to the environment) or internal (due to internal thoughts). To identify external distractions, previous researchers have used eye-gaze data. Eye-gaze data cannot, however, detect internal distractions because a user may be looking at the educational material in VR while also thinking about something else. We explored the usage of electroencephalogram (EEG) data to detect internal distractions. We designed an educational VR environment and trained three machine learning models: Random Forest (RF), Support Vector Machine (SVM), and k-nearest-neighbors (kNN), to detect internal distractions of students. For data labeling, we considered two window lengths (20 and 30 seconds) starting at 5 seconds after the distraction task started. We did cross-subject and cross-session tests, and our results show that kNN provides a better accuracy (64%) compared to RF and SVM. We also found that the shorter window length of 20 seconds provided a slightly better accuracy then the 30 second window. Our results are not far from such random guessing. Therefore, our contribution lies more in the fostering of ideas for future work that must employ more advanced and sophisticated techniques.
Asish et al. (Thu,) studied this question.