Integrating Temporal Information Into Knowledge Tracing: A Temporal Difference Approach

Abstract

Bayesian knowledge tracing (BKT), which is used to diagnose examinees’ knowledge state quantitatively, plays an important role in intelligent tutoring systems. Unfortunately, the temporal difference information of performance data, namely development of cognitive level, is not considered in BKT model, resulting in obvious cognitive diagnosis deviation. To eliminate the defect of BKT model so as to achieve accurate cognitive diagnosis, we proposed a temporal difference Bayesian knowledge tracing model (TD-BKT) to incorporate temporal difference information into knowledge tracing. Our model extracts temporal difference information by detecting cognitive inflection points where examinees’ cognitive level changes a lot, and then integrates those information into refined BKT model to quantify knowledge state accurately. Experiments were done on Junyi academy math practicing log dataset, comparing diagnostic precision of knowledge state between TD-BKT model and existing knowledge tracing models. The result turns out that our proposed TD-BKT model shows great improvement in assessing online examinees’ knowledge state quantitatively.