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

作　　者：李浩君[1] 高鹏 Li Haojun;Gao Peng(College of Education Science and Technology,Zhejiang University of Technology,Hangzhou 310023,Zhejiang,China)

机构地区：[1]浙江工业大学教育科学与技术学院,浙江杭州310023

出　　处：《计算机应用与软件》2024年第12期247-254,共8页Computer Applications and Software

基　　金：国家自然科学基金项目(62077043)。

摘　　要：学习者历史练习序列对当前作答有不同程度的影响,现有深度知识追踪模型对学习者学习迁移过程考虑相对不足。针对该问题,提出一种基于产生式迁移的深度知识追踪优化模型。该模型以产生式迁移理论为基础,用知识增长矩阵表示学习者练习后获得的知识和技能,以历史知识增长矩阵序列为输入,利用自注意力机制构建学习者学习迁移过程,根据包含学习迁移影响的值矩阵预测学习者正确回答下一问题的概率。实验结果表明,该模型提高了知识追踪的预测精度,且模型结果更具可解释性。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 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]