Cited by 388Online Learning and AnalyticsOnline and Blended LearningEducational Technology and E-Learning

Identifying significant indicators using LMS data to predict course achievement in online learning

You, Ji Won·2016·The Internet and Higher Education

Integrity signals

Registry

Crossref · 10.1016/j.iheduc.2015.11.003

Cites

51 of 51 references verified0 citation errors · 0 not checkable

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  • When the continuous assessment process of a course drastically changes, adding or reducing the number of activities or the typology of the exercises included, the GAR model is invalid because the historical data will be useless to predict learners' performance according to the new assessment model.

    “When the continuous assessment process of a course drastically changes, adding or reducing the number of activities or the typology of the exercises included, the GAR model is invalid because the historical data will be useless to predict learners' performance according to the new assessment model.”

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Authors
You, J. W.
Affiliations
Gachon University (KR)
Corresponding author
You, Ji Won
Venue
The Internet and Higher Education
Publisher
Elsevier BV
Published
2016-04
Volume
29
Pages
23-30
ISSN
1096-7516 (ISSN-L)
OpenAlex
W2183210442
Cited by
388
References
51
Citations by year
2026: 29 · 2025: 17 · 2024: 30 · 2023: 42 · 2022: 64 · 2021: 53 · 2020: 57 · 2019: 41 · 2018: 27 · 2017: 17 · 2016: 11
FWCI
27.60
Type
journal article
Language
en
Open access
closed
Locations
The Internet and Higher Education (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Social Sciences › Education › Online and Blended Learning
  • Physical Sciences › Computer Science › Information Systems › Educational Technology and E-Learning
Keywords
learning management system data, student self-regulation, university students, online learning

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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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