Using Data Mining Findings to Aid Searching for Better Cognitive Models

One key component of creating an intelligent tutoring system is forming a model that monitors student behavior. Researchers in machine learning area have been using automatic/semi-automatic techniques to search for cognitive models. One of the semi-automatic approaches is learning factor analysis, which involves human making hypothesis and identifying difficulty factors in the related items. In this paper, we propose a hybrid approach in which we leverage findings from our previous educational data mining work to aid the search for a better cognitive model and thus, improve the efficiency of LFA. Preliminary results suggest that our approach can lead to significantly better fitted cognitive models fast.