Automatic Detection of Student Mental Models During Prior Knowledge Activation in MetaTutor

This paper presents several methods to automatically detecting students' mental models in MetaTutor, an intelligent tutoring system that teaches students self-regulatory processes during learning of complex science topics. In particular, we focus on detecting students' mental models based on studentgenerated paragraphs during prior knowledge activation, a self-regulatory process. We describe two major categories of methods and combine each method with various machine learning algorithms. A detailed comparison among the methods and across all algorithms is also provided. The evaluation of the proposed methods is performed by comparing the prediction of the methods with human judgments on a set of 309 prior knowledge activation paragraphs collected from previous experiments with MetaTutor on college students. According to our experiments, a content-based method with word-weighting and Bayes Nets algorithm is the most accurate.

• Type: Journal
• Year: 2009
• Where: EDM
• Authors: Vasile Rus, Mihai C. Lintean, Roger Azevedo
• Keywords: Data Mining, EDM 2009, Mental Models, Prior Knowledge Activation, Students Self-regulatory Processes