The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Individualized Bayesian Knowledge Tracing Models
Open accessAbstract
Bayesian Knowledge Tracing (BKT)[1] is a user modeling method extensively used in the area of Intelligent Tutoring Systems. In the standard BKT implementation, there are only skill-specific parameters. However, a large body of research strongly suggests that student-specific variability in the data, when accounted for, could enhance model accuracy [5,6,8]. In this work, we revisit the problem of introducing student-specific parameters into BKT on a larger scale. We show that student-specific parameters lead to a tangible improvement when predicting the data of unseen students, and that parameterizing students’ speed of learning is more beneficial than parameterizing a priori knowledge.
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Cites
Knowledge tracing: Modeling the acquisition of procedural knowledge
Corbett, A. T., & Anderson, J. R. (1995) · User Modelling and User-Adapted Interaction · doi:10.1007/bf01099821 · Cited by 2,324
Cited by
Context-Aware Attentive Knowledge Tracing
Ghosh, A., Heffernan, N., & Lan, A. S. (2020) · Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3394486.3403282 · Cited by 580
AI-based personalized E-learning systems: Issues, challenges, and solutions
Murtaza, M., Ahmed, Y., Shamsi, J. A., Sherwani, F., & Usman, M. (2022) · IEEE Access · doi:10.1109/access.2022.3193938 · Cited by 386
Personalized Education in the Artificial Intelligence Era: What to Expect Next
Maghsudi, S., Lan, A., Xu, J., & van der Schaar, M. (2021) · IEEE Signal Processing Magazine · doi:10.1109/msp.2021.3055032 · Cited by 341
Park, H. W., Grover, I., Spaulding, S., Gomez, L., & Breazeal, C. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301687 · Cited by 146
Saint+: Integrating temporal features for ednet correctness prediction
Shin, D., Shim, Y., Yu, H., Lee, S., Kim, B., & Choi, Y. (2021) · LAK21: 11th International Learning Analytics and Knowledge Conference · doi:10.1145/3448139.3448188 · Cited by 141
Metadata
- Authors
- Yudelson, M. V., Koedinger, K. R., & Gordon, G. J.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2013
- Pages
- 171-180
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1959691478
- Cited by
- 483
- References
- 1
- FWCI
- 11.27
- Type
- conference paper
- Language
- en
- Open access
- green
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- Yudelson, M. V., Koedinger, K. R., & Gordon, G. J. (2013). Individualized Bayesian Knowledge Tracing Models. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-39112-5_18
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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