Adapting Is Difficult! Introducing a Generic Adaptive Learning Framework for Learner Modeling and Task Recommendation Based on Dynamic Bayesian Networks
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Bayesian mastery models predict a student's mastery by analyzing the expected number of correct responses.
“These models predict the student's mastery by analyzing the expected number of correct responses, thereby enabling the system to make some inferences about the ability of a student in general.”
From Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis (2024) · cites this paper as [102]
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Halkiopoulos, C., & Gkintoni, E. (2024) · Electronics · doi:10.3390/electronics13183762 · Cited by 328 · see citing works
Gnadlinger, F., Werminghaus, M., Selmanagić, A., Filla, T., Richter, J., Kriglstein, S., & Klenzner, T. (2024) · JMIR Serious Games · doi:10.2196/55231 · Cited by 7 · see citing works
“In addition to this described update of a specific observable variable, an inference process of the Bayesian network allows the system to update the related competencies [33].”
InfluentialSelf-citationСемеріков, С. О., Nechypurenko, P. P., Вакалюк, Т. А., et al. (2025) · Discover Education · doi:10.1007/s44217-025-01024-1 · Cited by 2 · see citing works
“The gradebook and rubrics provide detailed feedback mechanisms essential for formative assessment within a constructivist framework, while features like adaptive quiz questions and lesson branching allow content to adjust based on learner responses [17].”
Fedorka, P., Saibert, F., & Buchuk, R. (2024) · Вісник Черкаського державного технологічного університету · doi:10.62660/bcstu/3.2024.42
Mediengeschichtliche Einordnung und Einfluss von KI auf die zukünftige Entwicklung
Busch, C. (2025) · Springer eBooks · doi:10.1007/978-3-662-71567-3_4
- Authors
- Gnadlinger, F., Selmanagić, A., Simbeck, K., & Kriglstein, S.
- Affiliations
- Berliner Hochschule für Technik (DE)
- Masaryk University (CZ)
- Venue
- Proceedings of the 15th International Conference on Computer Supported Education
- Published
- 2023
- Pages
- 272-280
- OpenAlex
- W4366808710
- Cited by
- 5
- References
- 0
- Citations by year
- 2025: 2 · 2024: 3
- FWCI
- 0.89
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- International Conference on Computer Supported Education (publishedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- adaptive learning, student modeling, task recommendation, dynamic Bayesian networks, personalized 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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