FAST: Feature-Aware Student Knowledge Tracing

Various kinds of e-learning systems, such as Massively Open Online Courses and intelligent tu-toring systems, are now producing large amounts of feature-rich data from students solving items at different levels of proficiency over time. To analyze such data, researchers often use Knowl-edge Tracing [4], a 20-year old method that has become the de-facto standard for inferring student knowledge from performance data. Knowledge Tracing uses Hidden Markov Models (HMM) to estimate the latent cognitive state (student knowledge) from the students ’ performance answering items. Since the original Knowledge Tracing formulation does not allow to model general features, a considerable amount of research has focused on ad-hoc modifications to the Knowledge Tracing algorithm to enable modeling a specific feature of interest. This has led to a plethora of differ-ent Knowledge Tracing reformulations for very specific purposes. For example, Pardos et al. [5] proposed a new model to measure the effect of students ’ individual characteristics, Beck et al. [2] modified Knowledge Tracing to assess the effect of help in a tutor system, and Xu and Mostow [7] proposed a new model that allows measuring the effect of subskills. These ad hoc models are suc-cessful for their own specific purpose, but they do not generalize to arbitrary features. Other student modeling methods which allow more flexible features have been proposed. For example, Perfor