Folsom-Kovarik, J. T.


Transfer Learning in Intelligent Tutoring Systems — Results, Challenges and New Directions

AAAI Conferences

At the core of an intelligent tutoring system is the ability to estimate a student’s level of skill proficiency. However, making accurate skill estimates can require asking the student relatively many questions. We address this challenge by using “transfer learning,” a field of machine learning which uses data from related, but different, “source” domains to aid in learning in a poorly labeled “target” domain. Thus, to predict the skill of a student who hasn't answered many “target” skill questions, we use estimates of well tested “source” skills. We explore settings where the student has answered no questions related to the target skill (the cold start setting) and those where she has answered a few (the warm start setting). We focus on the challenging situation where the domain expert has not identified the relationship between the skills. We find that the Ridge estimator is useful for transferring knowledge from source to target skills, outperforming nonparametric regression methods and a baseline which only uses student performance on target skill questions.