Predicting the Determinants of Online Learning Adoption during the COVID-19 Outbreak: A Two-Staged Hybrid SEM-Neural Network Approach

Autor/inn/en	Thongsri, Nattaporn; Chootong, Chalothon; Tripak, Orawan; Piyawanitsatian, Piyaporn; Saengae, Rungtip
Titel	Predicting the Determinants of Online Learning Adoption during the COVID-19 Outbreak: A Two-Staged Hybrid SEM-Neural Network Approach
Quelle	In: Interactive Technology and Smart Education, 18 (2021) 3, S.362-379 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1741-5659
DOI	10.1108/ITSE-08-2020-0165
Schlagwörter	Electronic Learning; Higher Education; Independent Study; Learning Motivation; Computer Mediated Communication; Self Efficacy; Developing Nations; Foreign Countries; COVID-19; Pandemics; College Students; Structural Equation Models; Web Based Instruction; Readiness; Information Systems; Student Centered Learning; Thailand + Suchen Sie Ihr Suchwort? Hochschulbildung; Hochschulsystem; Hochschulwesen; Selbststudium; Motivation for studies; Lernmotivation; Computerkonferenz; Self-efficacy; Selbstwirksamkeit; Developing country; Developing countries; Entwicklungsland; Ausland; Collegestudent; Web Based Training; Group work; Student-entered learning; Student-centred learning; Student centred learning; Schülerorientierter Unterricht; Schülerzentrierter Unterricht; Gruppenarbeit
Abstract	Purpose: This study aims to study the adoption of online learning in higher education through the perspective of the readiness of the following factors: self-directed learning (SDL), motivation for learning (ML), online communication self-efficacy (OCE) and learner control (LC). This was an empirical study in the context of developing countries, specifically Thailand. Design/methodology/approach: This research applied a quantitative study method by collecting data from 605 higher education students in autonomous government institutions. The data analysis applied a structural equation model (SEM) to identify the significant determinants that affected the adoption of online learning. Moreover, this study applied a neural network model to examine the findings from the SEM. Findings: From the data analysis using the SEM and neural network model, the results matched each other. The results of the empirical study were firm and supported that the readiness factors of students had statistical significance in the following order: SDL, OCE, LC and ML. Practical implications: The study results showed an operational perspective to be prepared for online teaching, both for the related department of the Ministry of Education to support the infrastructure for online learning and for universities and instructors to create learning conditions and design teaching processes consistently with the online learning context. Originality/value: Since the learning management in the 21st century is focused on student-centred learning, the empirical results obtained from this study presented the view of learners' readiness that would influence the acceptance of online learning. In addition, this research presented the challenges and opportunities of online instruction during the COVID-19 pandemic. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01