Using correlational topic modeling for automated topic identification in intelligent tutoring systems

Student knowledge modeling is an important part of modern personalized learning systems, but typically relies upon valid models of the structure of the content and skill in a domain. These models are often developed through expert tagging of skills to items. However, content creators in crowdsourced personalized learning systems often lack the time (and sometimes the domain knowledge) to tag skills themselves. Fully automated approaches that rely on the covariance of correctness on items can lead to effective skill-item mappings, but the resultant mappings are often difficult to interpret. In this paper we propose an alternate approach to automatically labeling skills in a crowdsourced personalized learning system using correlated topic modeling, a natural language processing approach, to analyze the linguistic content of mathematics problems. We find a range of potentially meaningful and useful topics within the context of the ASSISTments system for mathematics problem-solving.