Integrating structural equation modelling and artificial neural networks to enhance predictive accuracy in understanding behavioural intentions towards blended learning systems

Sarkam, Nor Aslily and Mohamad Razi, Nor Faezah and Ahmad, Samsiah and Mohd Yusof, Mohammad Hafiz and Jamil, Nur Izzah and Hoon, Teoh Sian (2026) Integrating structural equation modelling and artificial neural networks to enhance predictive accuracy in understanding behavioural intentions towards blended learning systems. Asian Journal of University Education (AJUE), 22 (2). pp. 167-183. ISSN 1823-7797

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Abstract

Blended learning (BL) integrates technology with traditional teaching to enhance student engagement and learning outcomes. While BL offers numerous advantages, most research relies on linear models which limit the understanding of complex adoption factors. Conventional methods such as linear regression and Structural Equation Modelling (SEM) can reveal relationships between variables but often miss the more complex and nonlinear interactions that influence learning behaviours. Aiming to bridge this gap, the present study integrates Artificial Neural Networks (ANN) with SEM to enhance predictive accuracy and provide deeper analytical insights. The results indicate that effort expectancy (EE), performance expectancy (PE) and perceived playfulness (PP) contribute to blended learning adoption, with EE and PE driving adoption and PP enhancing user engagement. ANN improves prediction accuracy by capturing nonlinear relationships, reducing error margins and increasing explanatory power. The combined SEM-ANN approach offers a more comprehensive and precise analysis and helping educational institutions design more effective and engaging BL systems.

Item Type:	Article
Uncontrolled Keywords:	Artificial neural network, Behavioural intentions, Blended learning system, Structural equation modelling
Divisions:	Faculty of Technology Management and Technopreneurship
Depositing User:	Sabariah Ismail
Date Deposited:	20 Jul 2026 08:02
Last Modified:	20 Jul 2026 08:02
URI:	http://eprints.utem.edu.my/id/eprint/30138
Statistic Details:	View Download Statistic

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