Online Deep Knowledge Tracing

Abstract

This study focuses on solving the problem of knowledge tracing in a practical situation, where the responses from students come in a stream. Most current works of deep knowledge tracing are pursuing to integrate of more side information or data structure, but they often fail to make self-update in the dynamic learning situation. Towards this end, we here proposed an online deep knowledge tracing model, dubbed ODKT, by utilizing the online gradient descent algorithm to develop the traditional deep knowledge tracing (DKT) into online learning. Rather than learning a perfect model, the ODKT aims to train DKT in its using process step by step. Experiments were conducted on four public datasets for knowledge tracing. The results demonstrate that the ODKT model is effective and more suitable for practical applications.