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Diagnostic, predictive and compositional modeling with data mining in integrated learning environments
ARTICLE

Computers & Education Volume 49, Number 3, ISSN 0360-1315 Publisher: Elsevier Ltd

Abstract

Models represent a set of generic patterns to test hypotheses. This paper presents the CogMoLab student model in the context of an integrated learning environment. Three aspects are discussed: diagnostic and predictive modeling with respect to the issues of credit assignment and scalability and compositional modeling of the student profile in the context of an intelligent tutoring system/adaptive hypermedia learning system architectural pattern. The SOM–PCA, a collaborative-based data mining approach, is shown to be reusable for all three purposes above, enabling fast, objective implementations without requiring much intensive data collection.

Citation

Lee, C.S. (2007). Diagnostic, predictive and compositional modeling with data mining in integrated learning environments. Computers & Education, 49(3), 562-580. Elsevier Ltd. Retrieved September 27, 2020 from .

This record was imported from Computers & Education on January 30, 2019. Computers & Education is a publication of Elsevier.

Full text is availabe on Science Direct: http://dx.doi.org/10.1016/j.compedu.2005.10.010

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