A Outliers Analysis of Learnerapos;s Data based on User Interface Behaviors
Yong Se Kim; Tae Bok Yoon; Hyun Jin Cha; Young Mo Jung; Wang, E.; Jee Hyong Lee
Advanced Learning Technologies, 2007. ICALT 2007. Seventh IEEE International Conference on
Volume , Issue , 18-20 July 2007 Page(s):935 - 936
Digital Object Identifier   10.1109/ICALT.2007.25
Summary:A learning diagnosis system collects data from a learner's learning process, and analyzes it to build a suitable model for the learner, which can then be incorporated into an intelligent tutoring system to provide customized tutoring services. However, if the collected data reflects inconsistent learner behaviors or unpredictable learning tendencies, then the reliability of the learner model is degraded. In this paper, the outliers in the learner's data are eliminated by a k-NN method. We apply this method to an experimental data set obtained using DOLLS-HI, a learner diagnosis system that uses housing interior learning contents to diagnose learning styles. The resulting diagnosis model shows improved reliability than before eliminating the outliers.

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