Interpreting Deep Knowledge Tracing Model on EdNet Dataset
This work addresses interpretability in educational AI for researchers and practitioners, but it is incremental as it extends prior methods to a new dataset.
The authors applied existing interpretability techniques for deep knowledge tracing models to the large-scale EdNet dataset, demonstrating effectiveness in interpreting model predictions, though specific numerical results were not provided.
With more deep learning techniques being introduced into the knowledge tracing domain, the interpretability issue of the knowledge tracing models has aroused researchers' attention. Our previous study(Lu et al. 2020) on building and interpreting the KT model mainly adopts the ASSISTment dataset(Feng, Heffernan, and Koedinger 2009),, whose size is relatively small. In this work, we perform the similar tasks but on a large and newly available dataset, called EdNet(Choi et al. 2020). The preliminary experiment results show the effectiveness of the interpreting techniques, while more questions and tasks are worthy to be further explored and accomplished.