一种元学习增强的早期知识追踪建模方法MetaKTOA

Knowledge Tracing(KT)dynamically assesses and tracks students' knowledge mastery levels based on their historical learning trajectories,enabling the prediction of their future learning performance.As a core technology in online learning systems,KT facilitates personalized learning experiences.While existing deep neural network-based KT models(e.g.,DKT,DKVMN)have demonstrated significant advantages over traditional methods,they typically require large-scale training data.Early-stage interactions,where the student response data are extremely sparse,pose substantial challenges to training complex and effective deep KT models.To address this limitation,we propose MetaKT(Meta-Learning-Enhanced Knowledge Tracing),a framework that leverages meta-learning to enhance early-stage KT performance.Given a target KT task and several related auxiliary tasks,MetaKT first pre-trains the model on auxiliary tasks,and then fine-tunes it using the target task's limited data until convergence.Experiments on seven public datasets,with DKT and DKVMN as backbones,demonstrate that MetaKT improves AUC for DKT and DKVMN in 27 and 33 out of 35 test scenarios,respectively.