General Features in Knowledge Tracing: Applications to Multiple Subskills, Temporal Item Response Theory, and Expert Knowledge
José P. González-Brenes, Yun Huang, Peter L. Brusilovsky · 2014
Knowledge Tracing is the de-facto standard for inferring stu-dent knowledge from performance data. Unfortunately, it does not allow modeling the feature-rich data that is now possible to collect in modern digital learning environments. Because of this, many ad hoc Knowledge Tracing variants have been proposed to model a specific feature of interest. For example, variants have studied the effect of students’ individual characteristics, the effect of help in a tutor, and subskills. These ad hoc models are successful for their own specific purpose, but are specified to only model a single specific feature. We present FAST (Feature Aware Student knowledge Trac-ing), an efficient, novel method that allows integrating gen-eral features into Knowledge Tracing. We demonstrate FAST’s flexibility with three examples of feature sets that are rel-evant to a wide audience. We use features in FAST to model (i) multiple subskill tracing, (ii) a temporal Item Re-sponse Model implementation, and (iii) expert knowledge. We present empirical results using data collected from an Intelligent Tutoring System. We report that using features can improve up to 25 % in classification performance of the task of predicting student performance. Moreover, for fitting and inferencing, FAST can be 300 times faster than models created in BNT-SM, a toolkit that facilitates the creation of ad hoc Knowledge Tracing variants.