大数据2025，Vol.11Issue(5)：86-100,15.DOI:10.11959/j.issn.2096-0271.2025055
学习者学习行为建模:一种基于预训练模型的可解释性知识追踪模型
Learner behavior modeling:an interpretable knowledge tracking model based on pretrained model
摘要
Abstract
Knowledge tracing aims to track the transformation of learners'knowledge states during the learning process,thereby enhancing learners'learning efficiency.Recently,due to its significant importance in education,knowledge tracing has attracted considerable attention from researchers.However,deep learning-based knowledge tracing models primarily focus on achieving high accuracy in predicting learners'performance,but they significantly lack interpretability.Balancing the interpretability of knowledge tracing models while enhancing their performance remains a challenge.Based on this,a meticulously designed,high-performance,interpretable knowledge tracing model TIKT,was firstly proposed using item response theory(IRT)and Transformer.Subsequently,a fine-tuned pretrained model to predict the implicit difficulty levels of question texts to enhance data-side interpretability was used,and incorporated these difficulty levels into a contrastive learning framework to boost the overall performance of the knowledge tracing model.Experiments conducted on three real-world benchmark datasets conclusively demonstrate that our proposed model achieves improvements in both interpretability and performance relative to other models.关键词
知识追踪/对比学习/预训练模型Key words
knowledge tracing/contrastive learning/pretrained model分类
信息技术与安全科学引用本文复制引用
周涛,李艳婷,杨月婷,任俊霖,琚生根,师维..学习者学习行为建模:一种基于预训练模型的可解释性知识追踪模型[J].大数据,2025,11(5):86-100,15.基金项目
国家自然科学基金项目(No.62137001) The National Natural Science Foundation of China(No.62137001) （No.62137001）
