Global Feature-guided Knowledge Tracing

Knowledge tracing (KT) is a critical task in educational data mining, aiming to infer students' mastery levels of knowledge points using observable historical interaction data and related exercise information. However most of them only focus on local features, neglecting the utilization of students' overall learning ability features. To address this issue, we propose a novel KT model: Global Feature-guided Knowledge Tracing (GFKT). This model leverages students' historical interaction data to extract global features for guiding the training process, thereby improving the model's predictive capability. Specifically, (i) we design a global feature module to obtain students' overall learning abilities at the current moment. It constructs a data vector of ability values by calculating the difference between students' correct and incorrect response rates for each knowledge point, collecting this set of ability values as global features, (ii) we utilize Recurrent Neural Network to extract local features from students' exercise sequences, and propose a joint loss function that combines these local features with global features to train and optimize the model's performance. Extensive experiments on multiple real-world public datasets, GFKT demonstrates superior predictive performance compared to state-of-the-art KT methods.