Dynamic Cognitive Tracing: Towards Unified Discovery of Student and Cognitive Models

This work describes a unified approach to two problems previously addressed separately in Intelligent Tutoring Systems: (i) Cognitive Modeling, which factorizes problem solving steps into the latent set of skills required to perform them [7]; and (ii) Student Modeling, which infers students’ learning by observing student performance [9]. The practical importance of improving understanding of how students learn is to build better intelligent tutors [8]. The expected advantages of our integrated approach include (i) more accurate prediction of a student’s future performance, and (ii) clustering items into skills automatically, without expensive manual expert knowledge annotation. We introduce a unified model, Dynamic Cognitive Tracing, to explain student learning in terms of skill mastery over time, by learning the Cognitive Model and the Student Model jointly. We formulate our approach as a graphical model, and we validate it using sixty different synthetic datasets. Dynamic Cognitive Tracing significantly outperforms single-skill Knowledge Tracing on predicting future student performance.