Analyzing Brain Signals for Predicting Students’ Understanding of Online Learning: A Machine Learning Approach

doi:10.23940/ijpe.23.07.p5.462470

Abstract

Abstract: The primary focus of the educational process is on students who must comprehend the course material. Various methods predict a student's understanding, including questioning, exams, quizzes, feedback, and observing facial expressions. In an offline teaching-learning process, it is relatively straightforward to predict students' understanding. However, online learning, particularly while watching videos, presents challenges due to distractions in the surrounding environment. Predicting a student's understanding level in online learning becomes tedious as there is limited personal interaction between the student and the teacher. This study aims to identify an optimal machine learning model for predicting students' understanding of online learning by analyzing brain signals recorded using Electroencephalogram (EEG). The dataset comprises of brain signals collected from students of different ages and educational backgrounds. The GridsearchCV technique is utilized to select the optimal parameters. The experimental results demonstrate that KNN and SVM achieve nearly identical accuracy, approximately 99%, for predicting the understanding level.

Key words: Electroencephalogram (EEG), machine learning models, student understanding, exploratory data analysis

Harsha Gaikwad, Sanil Gandhi, Arvind Kiwelekar, and Manjushree Laddha. Analyzing Brain Signals for Predicting Students’ Understanding of Online Learning: A Machine Learning Approach [J]. Int J Performability Eng, 2023, 19(7): 462-470.

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