The DOI matches its Crossref record; 45 of the 48 references it cites were found in a registry or an index.
Effects of artificial intelligence-enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom
Overview
Integrity signals
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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?
In the citing paper
“AI-enabled recommendation agents can facilitate content personalization in videos to increase engagement and improve learning outcomes [153].”
From Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis (2024) · cites this paper as [153]
In the citing paper
“The integration of biofeedback mechanisms, which track and react to the physiological states of the learners, will enable personalized instruction strategies and improve their engagement and performance [153].”
From Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis (2024) · cites this paper as [153]
In the citing paper
“The integration of chatbots to eliminate learning anxiety has shown significant promise in most studies ([28,46,47]).”
In the citing paper
“These studies revealed that AI-based conversational chatbots significantly improved EFL learners’ engagement.”
Citations
Cites
Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions
Ryan, R. M., & Deci, E. L. (2000) · Contemporary Educational Psychology · doi:10.1006/ceps.1999.1020 · Cited by 18,666
Adomavičius, G., & Tuzhilin, A. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.99 · Cited by 10,347
Content-Based Recommendation Systems
Pazzani, M. J., & Billsus, D. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-72079-9_10 · Cited by 2,600
Sperling, A. P. (1982) · Elsevier eBooks · doi:10.1016/b978-0-434-98528-9.50018-9 · Cited by 1,568
Vision, challenges, roles and research issues of Artificial Intelligence in Education
Hwang, G.-J., Xie, H., Wah, B. W., & Gašević, D. (2020) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2020.100001 · Cited by 1,435
Recommendation systems: Principles, methods and evaluation
Isinkaye, F. O., Folajimi, Y. O., & Ojokoh, B. A. (2015) · Egyptian Informatics Journal · doi:10.1016/j.eij.2015.06.005 · Cited by 1,328
Mining LMS data to develop an “early warning system” for educators: A proof of concept
Macfadyen, L. P., & Dawson, S. (2010) · Computers & Education · doi:10.1016/j.compedu.2009.09.008 · Cited by 1,045
Measuring student engagement in technology-mediated learning: A review
Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015) · Computers & Education · doi:10.1016/j.compedu.2015.09.005 · Cited by 1,010
Hwang, G., & Chang, H.-F. (2010) · Computers & Education · doi:10.1016/j.compedu.2010.12.002 · Cited by 842
Intelligent tutoring systems and learning outcomes: A meta-analysis
Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014) · Journal of Educational Psychology · doi:10.1037/a0037123 · Cited by 840
Flagged first, then by citations
3 of the 48 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Huang, 2016, Effects of situated mobile learning approach on learning motivation and performance of EFL students, Journal of Educational Technology & Society, 19, 263
Cited by
Sorted by citations
Yilmaz, R., & Karaoglan Yilmaz, F. G. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100147 · Cited by 745
Song, C., & Song, Y. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1260843 · Cited by 717
Exploring the potential of artificial intelligence tools in educational measurement and assessment
Owan, V. J., Abang, K. B., Idika, D. O., Etta, E. O., & Bassey, B. A. (2023) · Eurasia Journal of Mathematics, Science and Technology Education · doi:10.29333/ejmste/13428 · Cited by 448
Lin, H., & Chen, Q. (2024) · BMC Psychology · doi:10.1186/s40359-024-01979-0 · Cited by 462
Will generative AI replace teachers in higher education? A study of teacher and student perceptions
Chan, C. K. Y., & Tsi, L. H. Y. (2024) · Studies in Educational Evaluation · doi:10.1016/j.stueduc.2024.101395 · Cited by 387
Metadata
- Authors
- Huang, A. Y. Q., Lu, O. H. T., & Yang, S. J. H.
- Affiliations
- National Central University (TW)
- National Chengchi University (TW)
- Corresponding author
- Yang, Stephen J.H.
- Venue
- Computers & Education
- Publisher
- Elsevier BV
- Published
- 2023-03
- Volume
- 194
- Pages
- 104684
- ISSN
- 0360-1315 (ISSN-L)
- OpenAlex
- W4310249545
- Cited by
- 605
- References
- 48
- Citations by year
- 2026: 149 · 2025: 233 · 2024: 171 · 2023: 47
- FWCI
- 87.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computers & Education (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Innovative Teaching Methods
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- flipped classroom, recommender systems, artificial intelligence in education, student engagement, student motivation, learning outcomes, learning management system data, student learning performance
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.
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