Extending Deep Knowledge Tracing: Inferring Interpretable Knowledge and Predicting Post-System Performance
This work addresses the need for more accurate knowledge inference in educational systems to better prepare students for future learning experiences.
The paper tackles the problem of student knowledge modeling by extending algorithms like Deep Knowledge Tracing (DKT) and DKVMN to directly infer student knowledge, resulting in knowledge estimates that correlate better with a posttest than existing methods such as Bayesian Knowledge Tracing (BKT) and Performance Factors Analysis (PFA).
Recent student knowledge modeling algorithms such as Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Networks (DKVMN) have been shown to produce accurate predictions of problem correctness within the same learning system. However, these algorithms do not attempt to directly infer student knowledge. In this paper we present an extension to these algorithms to also infer knowledge. We apply this extension to DKT and DKVMN, resulting in knowledge estimates that correlate better with a posttest than knowledge estimates from Bayesian Knowledge Tracing (BKT), an algorithm designed to infer knowledge, and another classic algorithm, Performance Factors Analysis (PFA). We also apply our extension to correctness predictions from BKT and PFA, finding that knowledge estimates produced with it correlate better with the posttest than BKT and PFA's standard knowledge estimates. These findings are significant since the primary aim of education is to prepare students for later experiences outside of the immediate learning activity.