Cited by 721Online Learning and AnalyticsInnovative Teaching MethodsRecommender Systems and Techniques

Effects of artificial intelligence-enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom

Huang, Anna Y.Q., Lu, Owen H.T., Yang, Stephen J.H.·2023·Computers & Education

Integrity signals

Registry

Crossref · 10.1016/j.compedu.2022.104684

Cites

45 of 45 references verified0 citation errors · 0 not checkable

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Recomputing p-values needs the paper’s full text.

Claims the paper makes

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Claims other papers make about this one

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Cites

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3 of the 48 references couldn’t be matched to a known paper.

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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 · 2022: 1 · 2021: 1
FWCI
87.22
Type
journal article
Language
en
Open access
closed
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.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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