AutoKT: Accelerating Knowledge Tracing Model Adaptation with Evolutionary Transfer Optimization
Longhui Li, Xiaobo Zhang, Hezhen Lu, Jie Zhang, Chenyang Bu · 2025
Knowledge tracing (KT) refers to the task of analyzing a student’s knowledge mastery over time based on sequential exercise-answering data and has been widely applied in educational settings, such as intelligent tutoring systems. Existing KT models often require manual selection of optimal hyperparameters for the current dataset. However, this manual adaptation process is both time-consuming and resource-intensive, limiting the scalability of KT models in practice. Although automatic architecture search can automate the discovery of optimal hyperparameters and model architectures, it is computationally expensive. This is especially true when the initial configuration is suboptimal, potentially requiring extensive search time to find satisfactory hyperparameters. To address these challenges, we explore the use of evolutionary transfer optimization to efficiently adapt KT models to new data. By integrating automated machine learning techniques with a genetic algorithm-based meta-learning approach, AutoKT transfers optimization knowledge from previously solved tasks to initialize model architectures and hyperparameters for new datasets. Experimental results demonstrate that this initialization can significantly reduce the computational cost of architecture search and hyperparameter tuning.