Detecting Distracted Students in an Educational VR Environment Utilizing Machine Learning on EEG and Eye-Gaze Data

Virtual Reality (VR) is frequently used in various educational contexts since it could improve knowledge retention compared to traditional learning methods. However, distraction is an unavoidable problem in the educational VR environment due to stress, mind-wandering, unwanted noise/sounds, irrelevant stimuli, etc. We explored the combination of EEG and eye gaze data to detect student distractions in an educational VR environment. We designed an educational VR environment and trained three machine learning models (CNN-LSTM, Random Forest and SVM) to detect distracted students. Our preliminary study results show that Random Forest and CNN-LSTM provide better accuracy (98%) compared to SVM.