Supervised vs Unsupervised Learning on Gaze Data to Classify Student Distraction Level in an Educational VR Environment
Sarker Monojit Asish, Arun K. Kulshreshth, Christoph W. Borst · Symposium on Spatial User Interaction · 2021
Educational VR may help students by being more engaging or improving retention compared to traditional learning methods. However, a student can get distracted in a VR environment due to stress, mind-wandering, unwanted noise, external alerts, etc. Student eye gaze can be useful for detecting these distraction. We explore deep-learning-based approaches to detect distractions from gaze data. We designed an educational VR environment and trained three deep learning models (CNN, LSTM, and CNN-LSTM) to gauge a student’s distraction level from gaze data, using both supervised and unsupervised learning methods. Our results show that supervised learning provided better test accuracy compared to unsupervised learning methods.