EEG-Based Analysis for Learning through Virtual Reality Environment

Abstract

Recently, many researchers studied learning through VR environment in various fields. Their assessment tools were based on tests, quizzes and/or statistical analysis of questionnaires. This study is based on the analysis of EEG signals collected from the students’ brains directly to capture their feelings and engagement during the lecture in both traditional and VR methods of teaching. To recognize the emotions of the students, the fine K-Nearest Neighbor (KNN) algorithm is used. To calculate the engagement score for a student, a well-known engagement score formula issued. The participants chosen are students of Anatomy and Physiology course. All participants were subject to three sessions of EEG signal acquisition for both Real Lecture and Virtual Reality, each session is five-minutes long. For better accuracy, EEG signals were captured three times for each student in each lecturing method. Based on the data-analyzing methods applied, which are Dependent Paired Samples T-Test and Independent Paired Samples T-Test, positive emotions in a real lecture are better than positive emotions in a VR-Lecture. However, the engagement score in both classes was approximately the same.