Predicting Students' Continuance Use of Learning Management System at a Technical University Using Machine Learning Algorithms

Autor/inn/en	Kuadey, Noble Arden; Mahama, Francois; Ankora, Carlos; Bensah, Lily; Maale, Gerald Tietaa; Agbesi, Victor Kwaku; Kuadey, Anthony Mawuena; Adjei, Laurene
Titel	Predicting Students' Continuance Use of Learning Management System at a Technical University Using Machine Learning Algorithms
Quelle	In: Interactive Technology and Smart Education, 20 (2023) 2, S.209-227 (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1741-5659
DOI	10.1108/ITSE-11-2021-0202
Schlagwörter	Foreign Countries; College Students; Learning Management Systems; Student Behavior; Predictor Variables; Artificial Intelligence; Algorithms; Electronic Learning; Models; Ghana + Suchen Sie Ihr Suchwort? Ausland; Collegestudent; Student behaviour; Schülerverhalten; Prädiktor; Künstliche Intelligenz; Algorithm; Algorithmus; Analogiemodell
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. (As Provided).
Anmerkungen	Emerald Publishing Limited. Howard House, Wagon Lane, Bingley, West Yorkshire, BD16 1WA, UK. Tel: +44-1274-777700; Fax: +44-1274-785201; e-mail: emerald@emeraldinsight.com; Web site: http://www.emerald.com/insight
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01