Applying Bayesian Network to Assess the Levels of Skills Mastering in Adaptive Dynamic OER-Systems
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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 [100]
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Halkiopoulos, C., & Gkintoni, E. (2024) · Electronics · doi:10.3390/electronics13183762 · Cited by 328 · see citing works
“The statistical approach helps determine the probability of the student passing a terminal test based on their performance against various items [100–103].”
- Authors
- Nekhaev, I., Zhuykov, I., Manukyants, S., & Maslennikov, A.
- Affiliations
- Volga State University of Technology (RU)
- Corresponding author
- Nekhaev, Igor
- Venue
- Advances in Intelligent Systems and Computing
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 1090-1116
- ISSN
- 2194-5357 (ISSN-L)
- 2194-5365
- OpenAlex
- W3112835264
- Cited by
- 3
- References
- 25
- Citations by year
- 2024: 1 · 2023: 1 · 2020: 1
- FWCI
- 0.19
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Advances in intelligent systems and computing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Computer Science Applications › Open Education and E-Learning
- Keywords
- open educational resources, open educational practices, adaptive learning systems, MOOCs, data-driven evaluation, learning analytics, prediction of learning outcomes
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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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