DeepIRT with a Hypernetwork to Optimize the Degree of Forgetting of Past Data
Tsutsumi, Emiko, Yiming Guo, Maomi Ueno · Zenodo (CERN European Organization for Nuclear Research) · 2022
Knowledge Tracing (KT), the task of tracing students' knowledge state, has attracted attention in the field of artificial intelligence. Recently, many researchers have proposed KT methods using deep learning to predict student performance on unknown tasks based on learning history data. Especially, the latest DeepIRT reportedly has high predictive accuracy and parameter interpretability. Nevertheless, some room remains for improvement of its prediction accuracy because it does not optimize the degree of forgetting of past data. Specifically, although its forgetting parameters are optimized solely using current input data, it should use both current input and past data to optimize the forgetting parameters. Therefore, for better parameter estimation to improve accuracy, this study proposes a new DeepIRT that optimizes the degree of forgetting of past data. The proposed method has a hypernetwork to balance both the current and the past data in memory, which stores a student's knowledge states. Results obtained from experimentation demonstrate that the proposed method improves the prediction accuracy and the interpretability of the students' ability compared to earlier KT methods.