Cited by 68Online Learning and AnalyticsEducational Technology and E-LearningIntelligent Tutoring Systems and Adaptive Learning

Effects and acceptance of precision education in an AI-supported smart learning environment

Hu, Yung-Hsiang·2022·Education and Information Technologies

Integrity signals

Registry

Crossref · 10.1007/s10639-021-10664-3

Cites

80 of 80 references verified0 citation errors · 0 not checkable

Statistics

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

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Cites

Flagged first, then by citations

3 of the 83 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Authors
Hu, Y.-H.
Affiliations
National Yunlin University of Science and Technology (TW)
Corresponding author
Hu, Yung-Hsiang
Venue
Education and Information Technologies
Published
2021-08-07
Volume
27
Issue
2
Pages
2013-2037
ISSN
  • 1360-2357 (ISSN-L)
  • 1573-7608
OpenAlex
W3191920449
Cited by
68
References
83
Citations by year
2026: 12 · 2025: 25 · 2024: 20 · 2023: 9 · 2022: 2
FWCI
6.56
Type
journal article
Language
en
Open access
closed
Locations
Education and Information Technologies (publishedVersion)
Funders
Ministry of Education (PGE1080215)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Physical Sciences › Computer Science › Information Systems › Educational Technology and E-Learning
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Keywords
precision education, learning analytics dashboards, smart learning environments, technology acceptance model, student learning behavior, learning outcomes, system quality, perceived ease of use, perceived usefulness, behavioral intention, transfer of learning, digital transformation of learning, first-year 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.

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