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Indonesian Journal of Electrical Engineering and Computer Science - 2023 : Machine Learning Prediction Of Video-Based Learning With Technology Acceptance Model

Rahayu, Abdul Rahman and Suraya, Masrom and NHA, Samad and Rulfah, M Daud (2023) Indonesian Journal of Electrical Engineering and Computer Science - 2023 : Machine Learning Prediction Of Video-Based Learning With Technology Acceptance Model. Indonesian Journal of Electrical Engineering and Computer Science. 0-0. ISSN 2502-4752

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Abstract

COVID-19 outbreak has significant impacts on education system as almost all countries shift to new way of teaching and learning; online learning. In this new environment, various innovative teaching methods have been created to deliver educational material in ensuring the learning outcomes such as video content. Thus, this research aims to implement machine learning prediction models for video-based learning in higher education institutions. Using survey data from 103 final year accounting students at Malaysian public university, this paper presents the fundamental frameworks of evaluating three machine learning models namely generalized linear model, random forest and decision tree. Besides demography attributes, the performance of each machine learning algorithm on the video-based learning usage has been observed based on the attributes of technology acceptance model namely perceived ease of use, perceived usefulness and attitude. The findings revealed that the perceived ease of use has given the highest weight of contributions to the generalized linear model and random forest while the major effects in decision tree has been given by the attitude variable. However, generalized linear model outperformed the two algorithms in term of the prediction accuracy.

Item Type: Article
Uncontrolled Keywords: COVID-19 pandemic Online learning Technology acceptance model Video content Video-based learning
Divisions: Institute of Graduate Studies (IGS)
Depositing User: LIBRARY1 UPTM
Date Deposited: 21 Jul 2025 01:59
Last Modified: 21 Jul 2025 01:59
URI: http://eprints.kuptm.edu.my/id/eprint/4904

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