The Impact on Individualizing Student Models on Necessary Practice Opportunities.

Abstract

When modeling student learning, tutors that use the Knowl-edge Tracing framework often assume that all students have the same set of model parameters. We find that when fitting parameters to individual students, there is significant varia-tion among the individual’s parameters. We examine if this variation is important in terms of instructional decisions by computing the difference in the expected number of prac-tice opportunities required if mastery is assessed using an individual student’s own estimated model parameters, com-pared to the population model. In the dataset considered, we find that a significant portion of students are expected to perform twice as many practice opportunities if the student is modeled using a population-based model, compared to the number needed if the student’s own model parameters were used. We also find an additional significant portion of stu-dents will be likely to receive less practice opportunities than needed, implying that such students will be advanced too early. Though further work on additional datasets is needed to explore this issue in more depth, our results suggest that considering individual variation in student parameters may have important implications for the instructional decisions made in intelligent tutoring systems that use a Knowledge Tracing model. 1.