Using Support Vector Machine on EEG Signals for College Students' Immersive Learning Evaluation

Abstract

Conventional methods such as questionnaires and scales to evaluate learners’ learning immersion are influenced by individuals’ subjective factors. The non-synchronism between the learning state and after-learning investigation also reduces the accuracy. We propose a new method to evaluate learners’ learning immersion based on electroencephalogram (EEG) and support vector machine (SVM). We construct 2 learning scenarios to induce immersive senses: VR video learning for high-level immersion and online English word learning for low-level immersion. To distinguish two immersion levels, students' EEGs are collected. After entering their attention score, relaxation score, the synchronization rate between the 2 scores, high alpha and low beta wave into SVM model, the precision accuracy reaches 87.80%. Taken the classified results and the participants’ self-reports together, we find VR devices can create a more immersive environment which improves learners’ learning effect. Our findings provide evidence supporting the feasibility of predicting learning immersion levels by physiological recordings.