Towards Predicting Future Transfer of Learning

We present an automated detector that can predict a student’s future performance on a transfer post-test, a post-test involving related but different skills than the skills studied in the tutoring system, within an Intelligent Tutoring System for College Genetics. We show that this detector predicts transfer better than Bayesian Knowledge Tracing, a measure of student learning in intelligent tutors that has been shown to predict performance on paper posttests of the same skills studied in the intelligent tutor. We also find that this detector only needs limited amounts of student data (the first 20% of a student’s data from a tutor lesson) in order to reach near-asymptotic predictive power.