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

LEE, C (2007) Diagnostic, predictive and compositional modeling with data mining in integrated learning environments. Computers & Education, 49 (3). 562-580 . ISSN 03601315

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Official URL: http://dx.doi.org/10.1016/j.compedu.2005.10.010

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. (C) 2005 Elsevier Ltd. All rights reserved.

Item Type: Article
Subjects: T Technology > T Technology (General)
Q Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 29 Sep 2011 07:01
Last Modified: 13 Feb 2014 09:07
URI: http://shdl.mmu.edu.my/id/eprint/2985

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