摘要
在教育数字化背景下,精准追踪学生的知识掌握程度成为教育数字化中提升教学质量的关键方法之一。知识追踪旨在通过对学生的多种行为数据(如试题作答情况、在线学习时长等)进行分析,评估学生对知识点的掌握程度。已有的知识追踪方法尽管在学生个性化学习表现预测中取得了较好的效果,但仍存在两方面的挑战:1)无法解决教育场景中普遍存在的数据稀疏问题;2)忽视学生知识获取的复杂动态过程,不能有效刻画知识的动态变化与遗忘规律。
In the context of educational digitalization,accurately tracking students’knowledge mastery has become one of the key approaches to improving teaching quality.Knowledge tracing seeks to analyze various types of student behavior data-such as responses to questions and online study duration-to evaluate their mastery of specific knowledge points.Although existing approaches have demonstrated good performance in predicting personalized learning behaviors,they still face two major challenges:1)the widespread issue of data sparsity in educational settings;2)the neglect of the complex and dynamic nature of students’knowledge acquisition,resulting in an inability to effectively capture changes in knowledge and forgetting patterns.To address these challenges,this paper proposes a personalized knowledge tracing model,DCL-FKT,which integrates dual contrastive lear-ning with a forgetting mechanism.The model alleviates data sparsity through question masking and substitution,building upon the traditional contrastive learning framework,it introduces a feature-level contrastive learning module to eliminate redundant representations and enhance modeling efficiency.In addition,by incorporating a forgetting gate mechanism,the model dynamically simulates the human forgetting curve,allowing it to accurately capture the nonlinear decay of students’knowledge over time and enabling dynamic modeling of the learning process.Experiments conducted on real-world datasets demonstrate that the proposed model achieves significant improvements in core metrics,such as prediction accuracy.It provides a more accurate reflection of students’actual knowledge levels and offers reliable support for personalized online learning.
作者
李春英
汤志康
庄郅玮
李文博
郭炎熙
张晓薇
LI Chunying;TANG Zhikang;ZHUANG Zhiwei;LI Wenbo;GUO Yanxi;ZHANG Xiaowei(School of Computer Science,Guangdong Polytechnic Normal University,Guangzhou 510665,China;Guangdong Provincial Key Laboratory of Intellectual Property&Big Data,Guangdong Polytechnic Normal University,Guangzhou 510665,China;School of Electronics and Information,Guangdong Polytechnic Normal University,Guangzhou 510665,China)
出处
《计算机科学》
北大核心
2026年第2期99-106,共8页
Computer Science
基金
国家自然科学基金(61807009)
广东省普通高校重点领域专项(2023ZDZX1009)
大语言模型技术与应用创新专项人才培养计划(粤教高函[2024]9号)
广东技术师范大学智能教育联合实验室项目(GSZLGC2023004)。
关键词
智慧教育
知识追踪
双重对比学习
遗忘机制
表现预测
Intelligent education
Knowledge tracing
Dual contrastive learning
Forgetting mechanism
Performance prediction
