Cognitive-aware lecture video recommendation system using brain signal in flipped learning pedagogy

Abstract

Various learning pedagogies have been developed, and they are adapted in a large number of institutes in various forms for improving learning ability of individual students. Flipped Learning (FL) model is one popular approach adopted in many higher learning institutions across the globe. In the flipped learning model, students take lesson from pre-loaded lecture videos before they solve critical problems in live classroom unlike other learning modes such as MOOCs (Massive Open Online Courses), Distance Learning, etc. However, student may not remain attentive throughout the video duration before solving critical problems in the live classroom. This may lead to serious learning incompetence over time in this learning pedagogy.In this paper, we analyze cognitive states of an individual student using brain waves signals while taking instructions in the absence of an instructor. The brain waves (Electroencephalogram (EEG)) signal is analyzed using unsupervised learning (clusters) techniques to group similar behaviors exhibited by student over video duration. Based on this analysis, we propose a recommendation technique which detects non-attentive video and suggests for retaking the lesson. This is termed as Lecture Video Recommendation in Flipped Learning (LRFL). We validate our approach with the data collected at our laboratory for the research purpose on flipped learning. Results demonstrate the effectiveness of our recommender technique.