Attention Classification and Lecture Video Recommendation Based on Captured EEG Signal in Flipped Learning Pedagogy

Abstract

Flipped learning (FL) utilizes blended learning approaches, where students first learn the lesson from preloaded lecture videos (i.e., online lectures). They complete their activities such as assignments, doubt clearing, practical work, real-life problemsolving inside classroom. Learning is directly connected to brain activities, and it becomes crucial to analyze the brain signals to identify the attention level of the learner. In order to analyze students' activity during the lesson, we capture the brain signals of the students and propose a framework for the feature extraction of brain wave (Electroencephalogram (EEG)) signals using variational autoencoder (VAE) in this article. The classification techniques are exploited to identify the weak students in the flipped learning scenario based on their cognitive state; subsequently, cognitive-aware lecture video recommendation system is developed to recommend the non-attentive lecture video/videos to the weak students. This study can be useful for instructors to identify learners who require special care to enhance their learning ability.