Cognitive-aware lecture video recommendation system using brain signal in flipped learning pedagogy. (30th November 2022)

• Record Type: Journal Article
• Authors: Shaw, Rabi; Patra, Bidyut Kr.
• Is Part Of: Expert systems with applications. Volume 207(2022)
• Publication Date: 2022-11-30
• Subjects: Cognitive-Aware Model (CAM) -- Dynamic Time Warping (DTW) -- Electroencephalography (EEG) -- Flipped Learning (FL) -- K-means clustering -- Recommender System (RS); Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118057
• Languages: English
• ISSNs: 0957-4174

Abstract

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 L ecture Video R ecommendation in F lipped L earning (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. Graphical abstract: Highlights: Brain waves of student are captured using single-channel headset at our laboratory. Brain signal is analyzed to find similar behavior exhibited over video duration. EEG data of learners is exploited to develop cognitive-aware recommender system. Attentive Index plays an important role in proposed lecture recommender system. Proposed method is evaluated using standard evaluation metrics on captured data.