Deep Knowledge Tracing

TL;DR

This paper demonstrates that Recurrent Neural Networks significantly improve the modeling of student knowledge in educational settings, enabling better predictions, curriculum design, and understanding of student learning patterns.

Contribution

It introduces the use of RNNs for knowledge tracing, showing they outperform previous methods and do not require explicit domain knowledge encoding.

Findings

RNN models outperform traditional methods in prediction accuracy.

Learned models facilitate curriculum design and interpretability.

RNNs capture complex student knowledge representations.

Abstract

Knowledge tracing---where a machine models the knowledge of a student as they interact with coursework---is a well established problem in computer supported education. Though effectively modeling student knowledge would have high educational impact, the task has many inherent challenges. In this paper we explore the utility of using Recurrent Neural Networks (RNNs) to model student learning. The RNN family of models have important advantages over previous methods in that they do not require the explicit encoding of human domain knowledge, and can capture more complex representations of student knowledge. Using neural networks results in substantial improvements in prediction performance on a range of knowledge tracing datasets. Moreover the learned model can be used for intelligent curriculum design and allows straightforward interpretation and discovery of structure in student tasks. These results suggest a promising new line of research for knowledge tracing and an exemplary application task for RNNs.