Journal article
Explaining Chinese university students' continuance learning intention in the MOOC setting: A modified expectation confirmation model perspective
Computers & Education, Vol.150, Article 103850
2020
Abstract
To gain more insight into the issue of high dropout rate in MOOC learning, this study aims at exploring the factors underlying the continuance intention to learn in the Massive Open Online Course (MOOC) setting. By modifying and extending the Expectation Confirmation Model (ECM), the authors propose a research model that includes cognitive and affective variables, captures reflections of the past and expectations for the future and takes into account both intrinsic and extrinsic motives in the model construction to explain learners' intention to persist in learning a MOOC. The proposed model was tested with data from Chinese university students. The results show that the proposed model can explain 48% of continuance intention. The new variables (attitude and curiosity) added to the ECM were all found to be significant in explaining continuance intention. This study deepens our understanding of the development of learners' continuance intention in the MOOC setting in the following aspects: (a) although the personal trait, curiosity, was found to predict subsequent continuance intention, attitude played a considerably dominant role. In addition to respecting individual differences, practitioners can devise appropriate interventions to change attitudes and influence learners' retention in MOOCs; (b) the strong link between confirmation and both satisfaction and attitude suggests that MOOC instructors or designers must be prudent in advertising the courses to avoid exaggerating their benefits and the system's affordances.
Details
- Title
- Explaining Chinese university students' continuance learning intention in the MOOC setting: A modified expectation confirmation model perspective
- Authors/Creators
- H.M. Dai (Author/Creator)T. Teo (Author/Creator)N.A. Rappa (Author/Creator)F. Huang (Author/Creator)
- Publication Details
- Computers & Education, Vol.150, Article 103850
- Publisher
- Elsevier
- Identifiers
- 991005542963907891
- Copyright
- © 2020 Elsevier Ltd.
- Murdoch Affiliation
- School of Education
- Language
- English
- Resource Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 6 Social Sciences
- 6.3 Management
- 6.3.368 Technology Acceptance Model
- Web Of Science research areas
- Computer Science, Interdisciplinary Applications
- Education & Educational Research
- ESI research areas
- Computer Science