Extracting Causal Relations in Deep Knowledge Tracing
This work provides a more accurate explanation of DKT's effectiveness for educational researchers and practitioners, potentially improving interpretability in knowledge tracing models.
The paper challenges the prevailing explanation that Deep Knowledge Tracing (DKT) models bidirectional relationships between knowledge components, demonstrating instead that its predictive strength stems from modeling prerequisite relationships as causal structures, with empirical evidence from the Assistments dataset showing alignment with causal subsets.
A longstanding goal in computational educational research is to develop explainable knowledge tracing (KT) models. Deep Knowledge Tracing (DKT), which leverages a Recurrent Neural Network (RNN) to predict student knowledge and performance on exercises, has been proposed as a major advancement over traditional KT methods. Several studies suggest that its performance gains stem from its ability to model bidirectional relationships between different knowledge components (KCs) within a course, enabling the inference of a student's understanding of one KC from their performance on others. In this paper, we challenge this prevailing explanation and demonstrate that DKT's strength lies in its implicit ability to model prerequisite relationships as a causal structure, rather than bidirectional relationships. By pruning exercise relation graphs into Directed Acyclic Graphs (DAGs) and training DKT on causal subsets of the Assistments dataset, we show that DKT's predictive capabilities align strongly with these causal structures. Furthermore, we propose an alternative method for extracting exercise relation DAGs using DKT's learned representations and provide empirical evidence supporting our claim. Our findings suggest that DKT's effectiveness is largely driven by its capacity to approximate causal dependencies between KCs rather than simple relational mappings.