Modeling the Learning That Takes Place Between Online Assessments

Abstract

Student models for adaptive learning environments and intelligent tutoring systems typically assume a paradigm of use where a student completes exercises or activities, and learns from those exercises or activities. However, many modern systems, including MOOCs, intersperse declarative content or lecture with assessment of the learning from this content. In this paper, we present a variant of a common student modeling algorithm, Bayesian Knowledge Tracing, which assumes that most learning occurs during use of declarative content rather than between exercises. We compare this algorithm’s predictive ability to classic Bayesian Knowledge Tracing and another common algorithm, Performance Factors Assessment. We find that our new algorithm, BKT-PL, performs slightly better than algorithms designed for the standard intelligent tutoring paradigm. Moreso, we can use BKT-PL to determine which declarative content is most and least effective, to drive iterative re-design.