Predicting Student Performance from Video-Based Learning System: A Case Study


Teoh, Chin Wei and Ho, Sin Ban and Dollmat, Khairi Shazwan and Tan, Chuie Hong (2022) Predicting Student Performance from Video-Based Learning System: A Case Study. Journal of Logistics, Informatics and Service Science, 9 (3). pp. 64-77. ISSN 2409-2665

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The major impact of the COVID-19 pandemic on the shift of education norms from physical classroom learning to MOOCs (Massive Open Online Courses) could accelerate the big data era growth for the e-learning platform. This circumstance has provided an opportunity for a teacher to use MOOC data to help students learn and perform better. Moreover, this research study goal is to propose a combination of machine learning algorithms and the feature selection benefit with the SMOTE (Synthetic Minority Oversampling Technique) algorithm for balancing the output features number to predict student performance in a video-based learning platform. As a result, the proposed machine learning classifier, Naïve Bayes algorithm with the combination of chi-square test and SMOTE has shown the highest accuracy in prediction of more than 90%. Results by the proposed classifier with feature selection and SMOTE have outperformed the traditional machine learning classifiers.

Item Type: Article
Uncontrolled Keywords: Machine learning, educational data mining (EDM), smote, student performance
Subjects: Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions: Faculty of Computing and Informatics (FCI)
Faculty of Management (FOM)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 01 Nov 2022 04:16
Last Modified: 01 Nov 2022 04:16


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