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Predicting students’ continuance use of learning management system at a technical university using machine learning algorithms

Noble Arden Kuadey (Department of Computer Science, Ho Technical University, Ho, Ghana; Center for West African Studies, University of Electronic Science and Technology of China, Chengdu, China and School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China)
Francois Mahama (Department of Mathematics and Statistics, Ho Technical University, Ho, Ghana)
Carlos Ankora (Department of Computer Science, Ho Technical University, Ho, Ghana)
Lily Bensah (Department of Computer Science, Ho Technical University, Ho, Ghana)
Gerald Tietaa Maale (Department of Information and Communication Technology, McCoy University College of Education, Nadowli, Ghana; Center for West African Studies, University of Electronic Science and Technology of China, Chengdu, China and School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China)
Victor Kwaku Agbesi (Center for West African Studies, University of Electronic Science and Technology of China, Chengdu, China and School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China)
Anthony Mawuena Kuadey (Department of Mathematics/ICT, St. Francis College of Education, Hohoe, Ghana)
Laurene Adjei (Department of Computer Science, Ho Technical University, Ho, Ghana)

Interactive Technology and Smart Education

ISSN: 1741-5659

Article publication date: 12 April 2022

Issue publication date: 9 May 2023

629

Abstract

Purpose

This study aims to investigate factors that could predict the continued usage of e-learning systems, such as the learning management systems (LMS) at a Technical University in Ghana using machine learning algorithms.

Design/methodology/approach

The proposed model for this study adopted a unified theory of acceptance and use of technology as a base model and incorporated the following constructs: availability of resources (AR), computer self-efficacy (CSE), perceived enjoyment (PE) and continuance intention to use (CIU). The study used an online questionnaire to collect data from 280 students of a Technical University in Ghana. The partial least square-structural equation model (PLS-SEM) method was used to determine the measurement model’s reliability and validity. Machine learning algorithms were used to determine the relationships among the constructs in the proposed research model.

Findings

The findings from the study confirmed that AR, CSE, PE, performance expectancy, effort expectancy and social influence predicted students’ continuance intention to use the LMS. In addition, CIU and facilitating conditions predicted the continuance use of the LMS.

Originality/value

The use of machine learning algorithms in e-learning systems literature has been rarely used. Thus, this study contributes to the literature on the continuance use of e-learning systems using machine learning algorithms. Furthermore, this study contributes to the literature on the continuance use of e-learning systems in developing countries, especially in a Ghanaian higher education context.

Keywords

Citation

Kuadey, N.A., Mahama, F., Ankora, C., Bensah, L., Maale, G.T., Agbesi, V.K., Kuadey, A.M. and Adjei, L. (2023), "Predicting students’ continuance use of learning management system at a technical university using machine learning algorithms", Interactive Technology and Smart Education, Vol. 20 No. 2, pp. 209-227. https://doi.org/10.1108/ITSE-11-2021-0202

Publisher

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Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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