Field-Aware Knowledge Tracing Machine by Modelling Students' Dynamic Learning Procedure and Item Difficulty
Wenbin Gan, Yuan Sun, Shiwei Ye, Yé Fan, Yi Sun · 2019
Knowledge tracing is essential for adaptive learning to obtain students' current knowledge states to provide adaptive service. However, the learning process is evolving constantly since students learn and forget over time. Moreover, the difficulty of learning materials can also have huge influence on their performances. This work shows an ongoing project on knowledge tracing by proposing a Field-Aware Knowledge Tracing Machine (FA-KTM) to integrate students' dynamic learning procedure (learning and forgetting) and adaptive item difficulty. We propose methods to model item difficulty and the dynamic learning procedure and present the framework to integrate them together. Preliminary analyses on a dataset show the promising results.