Interaction, internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
When teachers lack experience in using GenAI to support learning or organize collaborative activities, learners feel less engaged and less motivated.
“When teachers lack experience in using GenAI to support learning or organize collaborative activities, learners feel less engaged, less motivated (Kuo et al., 2014).”
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 Kuo et al., 2014
Cites
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483 · see citing works
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
Self-efficacy: Toward a unifying theory of behavioral change
Bandura, A. (1977) · Psychological Review · doi:10.1037/0033-295x.84.2.191 · Cited by 43,925 · see citing works
Motivational and self-regulated learning components of classroom academic performance.
Pintrich, P. R., & De Groot, E. V. (1990) · Journal of Educational Psychology · doi:10.1037/0022-0663.82.1.33 · Cited by 8,332 · see citing works
Zimmerman, B. J. (2000) · Handbook of Self-Regulation · doi:10.1016/b978-012109890-2/50031-7 · Cited by 6,069 · see citing works
Self-Efficacy Beliefs in Academic Settings
Pajares, F. (1996) · Review of Educational Research · doi:10.2307/1170653 · Cited by 4,688 · see citing works
A social cognitive view of self-regulated academic learning.
Zimmerman, B. J. (1989) · Journal of Educational Psychology · doi:10.1037/0022-0663.81.3.329 · Cited by 3,807 · see citing works
Reliability and Predictive Validity of the Motivated Strategies for Learning Questionnaire (Mslq)
Pintrich, P. R., Smith, D. A. F., Garcia, T., & Mckeachie, W. J. (1993) · Educational and Psychological Measurement · doi:10.1177/0013164493053003024 · Cited by 3,646 · see citing works
Sufficient Sample Sizes for Multilevel Modeling
Maas, C. J. M., & Hox, J. J. (2005) · Methodology · doi:10.1027/1614-2241.1.3.86 · Cited by 3,582 · see citing works
Cultivating competence, self-efficacy, and intrinsic interest through proximal self-motivation.
Bandura, A., & Schunk, D. H. (1981) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.41.3.586 · Cited by 2,675 · cited 1× · see citing works
“In contrast, low self-efficacy brings about inferior performance, and in turn decreases the sense of self-efficacy for a series of following relevant tasks (Bandura, 1977, 1982; Bandura & Schunk, 1981).”
BackgroundInformation searching strategies in web-based science learning: the role of internet self-efficacy
Tsai, M.-J., & Tsai, C.-C. (2003) · Innovations in Education and Teaching International · doi:10.1080/1355800032000038822 · Cited by 470 · see citing works
Nicol, D. (2009) · Assessment & Evaluation in Higher Education · doi:10.1080/02602930802255139 · Cited by 272 · see citing works
Identifying at‐risk students in videoconferencing and web‐based distance education
Osborn, V. (2001) · American Journal of Distance Education · doi:10.1080/08923640109527073 · Cited by 135 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Predicting student satisfaction and perceived learning within online learning environments
Alqurashi, E. (2019) · Distance Education · doi:10.1080/01587919.2018.1553562 · Cited by 697 · see citing works
A systematic review of research on online teaching and learning from 2009 to 2018
Martin, F., Sun, T., & Westine, C. D. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.104009 · Cited by 616 · 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
Fabriz, S., Mendzheritskaya, J., & Stehle, S. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.733554 · Cited by 339 · see citing works
Effects of teacher role on student engagement in WeChat-Based online discussion learning
Xu, B., Chen, N.-S., & Chen, G. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.103956 · Cited by 271 · see citing works
Self–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
Soffer, T., & Nachmias, R. (2018) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12258 · Cited by 174 · see citing works
Teacher Training Institute, M., Cheok, M. L., Wong, S. L., & Faculty of Educational Studies, U. P. M. (2015) · International Journal of Instruction · doi:10.12973/iji.2015.816a · Cited by 127 · see citing works
The Impact of AI Usage on University Students’ Willingness for Autonomous Learning
Wang, L., & Li, W. (2024) · Behavioral Sciences · doi:10.3390/bs14100956 · Cited by 94 · see citing works
Why college students procrastinate in online courses: A self-regulated learning perspective
Cheng, S.-L., & Xie, K. (2021) · The Internet and Higher Education · doi:10.1016/j.iheduc.2021.100807 · Cited by 93 · see citing works
Spencer, D., & Temple, T. (2021) · Online Learning · doi:10.24059/olj.v25i2.2227 · Cited by 67 · see citing works
Effectiveness of blended learning in the first year of fashion education
Yick, K.-L., Yip, J., Au, S.-C., Lai, Y.-Y., & Yu, A. (2019) · International Journal of Fashion Design, Technology and Education · doi:10.1080/17543266.2018.1546910 · Cited by 40 · see citing works
Measuring Learner Satisfaction of an Adaptive Learning System
Lim, L., Lim, S. H., & Lim, R. W. Y. (2022) · Behavioral Sciences · doi:10.3390/bs12080264 · Cited by 37 · see citing works
A Systematic Review of Studies Exploring Help-Seeking Strategies in Online Learning Environments
Yang, F., & Stefaniak, J. (2023) · Online Learning · doi:10.24059/olj.v27i1.3400 · Cited by 27 · 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
- Kuo, Y.-C., Walker, A. E., Schroder, K. E. E., & Belland, B. R.
- Affiliations
- Jackson State University (US)
- Utah State University (US)
- Corresponding author
- Kuo, Yu-Chun
- Venue
- The Internet and Higher Education
- Publisher
- Elsevier BV
- Published
- 2014-01
- Volume
- 20
- Pages
- 35-50
- ISSN
- 1096-7516 (ISSN-L)
- OpenAlex
- W2089209769
- Cited by
- 1,082
- References
- 13
- Citations by year
- 2026: 71 · 2025: 94 · 2024: 140 · 2023: 173 · 2022: 168 · 2021: 153 · 2020: 89 · 2019: 52 · 2018: 45 · 2017: 33 · 2016: 36 · 2015: 22 · 2014: 5 · 2013: 1
- FWCI
- 96.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Internet and Higher Education (publishedVersion)
- Topics
- Social Sciences › Education › Online and Blended Learning
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Technology-Enhanced Education Studies
- Keywords
- student satisfaction, online learning, learner-instructor interaction, learner-content interaction, student-student interaction, internet self-efficacy, student self-regulation, distance education, undergraduate and graduate students
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
