Selecting Baysian-Network Models Based on Simulated Expectation

Abstract

Identifying the best network structure from a myriad of candidates is not an easy task, and we propose a supervised learning method for this task. We test the idea with an instance of learning student models from students’ responses to test items, because student models are very important for intelligent tutoring systems. The training data for the classifiers were simulated based on the expectation about students’ item responses when students learn in different ways, and the trained classifier was used to select the model from the list of candidate models based on the observed item responses. Experimental results indicate that, even when item responses do not faithfully reflect students’ competence in the concepts, our classifiers still help us differentiate very similar models with indirect observations.