DF-EGM: Personalised Knowledge Tracing with Dynamic Forgetting and Enhanced Gain Mechanisms
Hongyun Wang, Leilei Shi, Zixuan Han, Lu Liu, Xiang Sun, Furqan Aziz · 2024
In current online education, Knowledge Tracing (KT) technology plays a pivotal role, in monitoring and updating students' evolving knowledge states throughout their educational journey. Previous knowledge tracing models like DKT and DKVMN, have made some progress in personalised learning. However, they fall short in fully capturing the dynamic changes in the learning process, including forgetting behaviour and individual learning gains. To address these issues, this paper introduces a novel knowledge tracing model named dynamic forgetting and enhanced gain mechanisms (DF-EGM), which builds on Deep-Irt. The model initially utilises a CART decision tree classifier to extract pre-classification labels by analyzing response times and learning abilities. Subsequently, the model incorporates forgetting factors and personalised learning gains. It introduces attempt counts and time intervals to simulate knowledge forgetting. and the realization of personalised learning gains is achieved through a deeply refined knowledge gain module. This module models the perceived difficulty of exercises by students and dynamically adjusts learning gains based on their answer performance and current knowledge state. Experiments on public datasets indicate that the DF-EGM model can more accurately track students' knowledge states.