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Permanent link (DOI): https://doi.org/10.7939/R35H7C71D

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A neural network approach to estimate student skill mastery in cognitive diagnostic assessments Open Access

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Author or creator
Guo, Q.
Cutumisu, M.
Cui, Y.
Additional contributors
Subject/Keyword
Student modeling
Prerequisite discovery
Skills
Cognitive diagnosis model
Neural network
Type of item
Conference/workshop Poster
Language
English
Place
Time
Description
In computer-based tutoring systems, it is important to assess students’ mastery of different skills and provide remediation. In this study, we propose a novel neural network approach to estimate students’ skill mastery patterns. We conducted a simulation to evaluate the proposed neural network approach and we compared the neural network approach with one of the most widely used cognitive diagnostic algorithm, the DINA model, in terms of skill estimation accuracy and the ability to recover skill prerequisite relations. Results suggest that, while the neural network method is comparable in skill estimation accuracy to the DINA model, the former can recover skill prerequisite relations more accurately than the DINA model.
Date created
2017
DOI
doi:10.7939/R35H7C71D
License information
Attribution-NonCommerical-NoDerivs 4.0 International
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Citation for previous publication
Guo, Q., Cutumisu, M., and Cui, Y. (2017). A neural network approach to estimate student skill mastery in cognitive diagnostic assessments. Proceedings of the 10th International Educational Data Mining Conference, (), .
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