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Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems
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“AI could enhance adaptive teaching and learning when using personalized learning platforms that adapt to students' learning strategies, the sequence and difficulty of the task abilities, the time of feedback and students' preferences (Pliakos et al., 2019).”
From A systematic review of literature reviews on artificial intelligence in education (AIED): a roadmap to a future research agenda (2024) · cites this paper as Pliakos et al., 2019
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Metadata
- Authors
- Pliakos, K., Joo, S.-H., Park, J. Y., Cornillie, F., Vens, C., & Van den Noortgate, W.
- Affiliations
- IMEC (BE)
- Corresponding author
- Park, Jung Yeon
- Venue
- Computers & Education
- Publisher
- Elsevier BV
- Published
- 2019-08
- Volume
- 137
- Pages
- 91-103
- ISSN
- 0360-1315 (ISSN-L)
- OpenAlex
- W2936389135
- Cited by
- 149
- References
- 87
- Citations by year
- 2026: 19 · 2025: 25 · 2024: 32 · 2023: 12 · 2022: 26 · 2021: 18 · 2020: 17
- FWCI
- 6.88
- Type
- journal article
- Language
- en
- Open access
- green
- lirias.kuleuven.be
- Licence
- Locations
- Computers & Education (publishedVersion)
- Lirias (submittedVersion, other-oa)
- Funders
- imec
- Agentschap Innoveren & Ondernemen
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Keywords
- cold start, adaptive learning systems, item response theory, ability estimation, random forest, classification and regression trees
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 ›
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