Predicting students' continuance use of learning management system at a technical university using machine learning algorithms
Interactive Technology and Smart Education
; 20(2):209-227, 2023.
Article
in English
| ProQuest Central | ID: covidwho-2317714
ABSTRACT
PurposeThis 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/approachThe 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.FindingsThe 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/valueThe 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.
Technology: Comprehensive Works; Higher education; E-learning; Learning management systems; UTAUT; Machine learning algorithms; Developing countries; Teaching; Success; Developing countries--LDCs; University students; Machine learning; Management systems; Colleges & universities; Students; COVID-19; Hypotheses; Pandemics; Online instruction; Algorithms; Multivariate statistical analysis; Literature reviews; Coronaviruses; Distance learning; Disease transmission; Saudi Arabia; Ghana; Indonesia; India
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Type of study:
Prognostic study
Language:
English
Journal:
Interactive Technology and Smart Education
Year:
2023
Document Type:
Article
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