南京大学学报（自然科学版）2026，Vol.62Issue(2)：267-276,10.DOI:10.13232/j.cnki.jnju.2026.02.009
一种元学习增强的早期知识追踪建模方法MetaKT
MetaKT:A Meta-learning-enhanced early-stage knowledge tracing approach
摘要
Abstract
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.关键词
元学习/深度知识追踪/小样本学习/MAML/个性化学生建模Key words
meta learning/deep knowledge tracing/few shot learning/MAML/personalized student modeling分类
信息技术与安全科学引用本文复制引用
王晶,苏健华,马玉玲,于德湖,崔超然,于志云..一种元学习增强的早期知识追踪建模方法MetaKT[J].南京大学学报（自然科学版）,2026,62(2):267-276,10.基金项目
国家自然科学基金(62177031),山东省自然科学基金(ZR2021MF044),2023年度教育部人文社会科学研究专项任务(高校辅导员研究)(2023JDSZ3174),山东建筑大学国内访问学者经费 （62177031）
