计算机应用与软件2024，Vol.41Issue(12)：247-254,8.DOI:10.3969/j.issn.1000-386x.2024.12.035
基于产生式迁移的深度知识追踪优化模型
DEEP KNOWLEDGE TRACKING OPTIMIZATION MODEL BASED ON PRODUCTION TRANSFER THEORY
摘要
Abstract
The learner's historical practice sequence has varying degrees of influence on the current answer,and the existing deep knowledge tracking model is relatively insufficient to consider the learner's learning transfer process.Aimed at this problem,a deep knowledge tracking optimization model based on production transfer theory is proposed.Based on the theory of production transfer,the model used a knowledge growth matrix to represent the knowledge and skills acquired by learners after practice.It took the historical knowledge growth matrix sequence as input and used the self-attention mechanism to construct the learner's learning transfer process.The influence value matrix predicted the probability that the learner would answer the next question correctly.Experimental results show that the model improves the prediction accuracy of knowledge tracking,and the model structure is more interpretable.关键词
知识追踪/学习迁移/深度学习/自注意力机制Key words
Knowledge tracking/Learning transfer/Deep learning/Self-Attention mechanism分类
信息技术与安全科学引用本文复制引用
李浩君,高鹏..基于产生式迁移的深度知识追踪优化模型[J].计算机应用与软件,2024,41(12):247-254,8.基金项目
国家自然科学基金项目(62077043). （62077043）
