Student Progress Modeling with Skills Deficiency Aware Kalman Filters
Carlotta Schatten, Lars Schmidt-Thieme · 2016
One new usage of Learning Analytics in Intelligent Tutoring Systems (ITS) is sequencing based on performance prediction, which informs sequencers whether a student mastered or not a specific set of skills. Matrix Factorization (MF) performance prediction is particularly appealing because it does not require tagging involved skills in tasks. However, MF’s difficult interpretability does not allow to show the student’s state evolution, i.e. his/her progress over time. In this paper we present a novel progress modeling technique integrating the most famous control theory state modeler, the Kalman Filter, and Matrix Factorization. Our method, the Skill Deficiency aware Kalman State Estimation for Matrix Factorization, (1) updates at each interaction the student’s state outperforming the baseline both in prediction error and in computational requirements allowing faster online interactions; (2) models the individualized progress of the students over time that could be later used to develop novel sequencing policies. Our results are tested on data of a commercial ITS where other state of the art methods were not applicable.