The DOI matches its Crossref record; 84 of the 87 references it cites were found in a registry or an index, and 1 are not checked yet.
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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- Brusilovsky, 1999, Vol. 4, 19
- Vie, 2018, International Journal of Artificial Intelligent Education
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Application and theory gaps during the rise of artificial intelligence in education
Chen, X., Xie, H., Zou, D., & Hwang, G.-J. (2020) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2020.100002 · Cited by 1,003
Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review
Gligorea, I., Cioca, M., Oancea, R., Gorski, A.-T., Gorski, H., & Tudorache, P. (2023) · Education Sciences · doi:10.3390/educsci13121216 · Cited by 980
AI-enabled adaptive learning systems: A systematic mapping of the literature
Kabudi, T., Pappas, I., & Olsen, D. H. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100017 · Cited by 734
A systematic literature review of personalized learning terms
Shemshack, A., & Spector, J. M. (2020) · Smart Learning Environments · doi:10.1186/s40561-020-00140-9 · Cited by 740
“Ennouamani, Mahani and Akharraz (2020) argued that learners are different in terms of their needs, knowledge, personality, behavior (Pliakos et al., 2019) preferences, learning style, culture, as well as the parameters of the mobile devices that they use.”
BackgroundMustafa, M. Y., Tlili, A., Lampropoulos, G., Huang, R., Jandrić, P., Zhao, J., Salha, S., Xu, L., Panda, S., Kinshuk, et al. (2024) · Smart Learning Environments · doi:10.1186/s40561-024-00350-5 · Cited by 235
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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