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Improvement of Learner's learning Style Diagnosis System using Visualization Method  

Yoon, Tae-Bok (성균관대학교 컴퓨터공학과)
Choi, Mi-Ae (성균관대학교 교육학과)
Lee, Jee-Hyong (성균관대학교 컴퓨터공학과)
Kim, Yong-Se (성균관대학교 창의연구단)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.15, no.3, 2009 , pp. 226-230 More about this Journal
Abstract
Intelligent Tutoring System (ITS) is a procedure of analyzing collected data for teaming, making a strategy and performing adequate service for learners. To perform suitable service for learners, modeling is the first step to collect data from the process of their learning. The model, however, cannot be authentic if collected data can contain learners' inconsistent behaviors or unpredictable learning inclination. This study focused on how to sort normal and abnormal data by analyzing collected data from learners through visualization. A model has been set up to assort unusual data from collected learner's data by using DOLLS-HI which makes possible to diagnose learner's learning propensity based on housing interior learning contents in the experiment. The created model has been confirmed its improved reliability comparing to previous one.
Keywords
Intelligent Tutoring System; Leaner Modeling; Visualization Analysis Method;
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