DeepIRT to optimize the degree of forgetting past data

Abstract

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. Especially, the latest DeepIRT reportedly has high prediction 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. To resolve that difficulty, this study proposes a new DeepIRT with hyper-network that optimizes the degree of forgetting of past data using both the current and the past data. 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.