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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
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