Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments
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Learning activities with higher interactivity will increase motivation, improve learning outcomes and satisfaction compared to less interactive learning environments.
“Learning activities with higher interactivity will increase motivation, improve learning outcomes and satisfaction compared to less interactive learning environments (Liaw and Huang, 2013).”
From Exploring the factors influencing college students’ learning satisfaction in generative AI-supported MOOCs learning environment: a learning experience framework perspective (2025) · cites this paper as Liaw and Huang, 2013
Cites
Self-efficacy: The exercise of control
Bandura, A., Freeman, W. H., & Lightsey, R. (1999) · Journal of Cognitive Psychotherapy · doi:10.1891/0889-8391.13.2.158 · Cited by 46,835 · see citing works
Structural equation modeling in practice: A review and recommended two-step approach
Anderson, J. C., & Gerbing, D. W. (1988) · Psychological Bulletin · doi:10.1037/0033-2909.103.3.411 · Cited by 41,941 · see citing works
Presno, C. (1998) · Journal of Educational Computing Research · doi:10.2190/uy72-5tg8-0lt5-au4l · Cited by 84 · see citing works
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Evaluating E-learning systems success: An empirical study
Al-Fraihat, D., Joy, M., Masa'deh, R., & Sinclair, J. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2019.08.004 · Cited by 1,350 · see citing works
Online learning performance and satisfaction: do perceptions and readiness matter?
Wei, H.-C., & Chou, C. (2020) · Distance Education · doi:10.1080/01587919.2020.1724768 · Cited by 529 · see citing works
Yilmaz, R. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2016.12.085 · Cited by 514 · see citing works
Tracking e-learning through published papers: A systematic review
Rodrigues, H., Almeida, F., Figueiredo, V., & Lopes, S. L. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.03.007 · Cited by 399 · see citing works
Pan, X. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.564294 · Cited by 329 · see citing works
“Liaw and Huang (2013) conducted a relatively successful theoretical innovation by integrating TAM and self-regulated learning theory, revealing the structural relationship between learners’ perceived usefulness, satisfaction, and self-regulated learning.”
BackgroundSelf–efficacy and student satisfaction in the context of blended learning courses
Prifti, R. (2022) · Open Learning: The Journal of Open, Distance and e-Learning · doi:10.1080/02680513.2020.1755642 · Cited by 175 · see citing works
Lv, P., Wang, X., Nie, Y., Wu, R., & Wang, L. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1633686 · Cited by 5 · see citing works
- Authors
- Liaw, S.-S., & Huang, H.-M.
- Affiliations
- China Medical University (TW)
- Corresponding author
- Liaw, Shu-Sheng
- ORCID
- Huang, Hsiu-Mei
- Venue
- Computers & Education
- Publisher
- Elsevier BV
- Published
- 2013-01
- Volume
- 60
- Issue
- 1
- Pages
- 14-24
- ISSN
- 0360-1315 (ISSN-L)
- OpenAlex
- W2043716431
- Cited by
- 571
- References
- 3
- Citations by year
- 2026: 33 · 2025: 46 · 2024: 59 · 2023: 73 · 2022: 70 · 2021: 68 · 2020: 50 · 2019: 32 · 2018: 25 · 2017: 32 · 2016: 34 · 2015: 28 · 2014: 10 · 2013: 11
- FWCI
- 76.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computers & Education (publishedVersion)
- Topics
- Social Sciences › Education › Online and Blended Learning
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Social Sciences › Education › Technology-Enhanced Education Studies
- Keywords
- student self-regulation, online learning, perceived satisfaction, perceived usefulness, interactive learning environment, self-efficacy, perceived anxiety, student attitudes, university students, conceptual model
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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