沈阳航空航天大学学报2024，Vol.41Issue(2)：47-56,10.DOI:10.3969/j.issn.2095-1248.2024.02.006
融合习题特征信息的交叉注意力机制知识追踪模型
Knowledge tracing model with integrating exercise feature information cross-attention mechanism
摘要
Abstract
Tracing learners'mastery of knowledge is a pivotal research direction in the realm of wisdom education.Traditional deep knowledge tracing methods predominantly focus on recurrent neural net-works,facing challenges such as the lack of interpretability and handling long sequence dependencies.Additionally,many methods overlook the influence of learner characteristics and exercise features on experimental results.Addressing these issues,a cross-attention mechanism knowledge tracing model was proposed.The model integrated knowledge points and exercise features information to obtain a question feature embedding module.Subsequently,improvements were made to the attention mecha-nism based on learner responses,resulting in a dual attention mechanism module.To account for real ex-ercise-solving situations,a guess-error module based on attention mechanisms was introduced.firstly,the model took in exercise features information,obtaining a learner response with integrating exercise information through the exercise features embedding module.Following processing by the guess-error module,authentic learner responses were derived.Finally,the prediction module yielded the probability of a learner answering correctly in the next instance.Experimental results demonstrate that the cross-at-tention knowledge tracing model,incorporating exercise features,outperformed the traditional dynamic keyhole transformer(DKT)model,with 3.13%increase in AUC and 3.44%increase in ACC.This mod-el proves effective in handling long sequence dependencies while exhibiting enhanced interpretability and predictive performance.关键词
智慧教育/知识追踪/交叉注意力机制/循环神经网络/习题特征信息Key words
wisdom education/knowledge tracing/cross-attention mechanism/recurrent neural net-work/exercise features information分类
信息技术与安全科学引用本文复制引用
张翼飞,关凯俊,张加金..融合习题特征信息的交叉注意力机制知识追踪模型[J].沈阳航空航天大学学报,2024,41(2):47-56,10.基金项目
国家自然科学基金(项目编号:62102271) （项目编号:62102271）
