Transferable Student Performance Modeling for Intelligent Tutoring Systems

Abstract

Millions of students worldwide are now using intelligent tutoring systems (ITSs). At their core, ITSs rely on student performance models (SPMs) to trace each student's changing ability level over time, in order to provide personalized feedback and instruction. Crucially, SPMs are trained using interaction sequence data of previous students to analyze data generated by future students. This induces a cold-start problem when a new course is introduced, because no students have yet taken the course and hence there is no data to train the SPM. Here, we consider transfer learning techniques to train accurate SPMs for new courses by leveraging log data from existing courses. We study two settings: (i) In the naive transfer setting, we first train SPMs on existing course data and then apply these SPMs to new courses without modification. (ii) In the inductive transfer setting, we fine tune these SPMs using a small amount of training data from the new course (e.g., collected during a pilot study). We evaluate the proposed techniques using student interaction sequence data from five different mathematics courses taken by over 47,000 students. The naive transfer models that use features provided by human domain experts (e.g., difficulty ratings for questions in the new course) but no student interaction training data for the new course, achieve prediction accuracy on par with standard BKT and PFA models that use training data from thousands of students in the new course. In the inductive setting our transfer approach yields more accurate predictions than conventional SPMs when only limited student interaction training data (<100 students) is available to both.