|저자||Yongse Kim, Tae Bok Yoon, Hyunjin Cha, Wang Eric,and Jee-Hyong Lee|
|학회명||IEEE International Conference on Advanced Learning Technologies|
|학회명 (약자)||ICALT 2007|
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.