基于个性化学习和深层次细化的知识追踪  

Knowledge tracing based on personalized learning and deep refinement

作　　者：李林昊 张晓倩 董瑶 王旭[1,2,3] 董永峰 LI Linhao;ZHANG Xiaoqian;DONG Yao;WANG Xu;DONG Yongfeng(School of Artificial Intelligence,Hebei University of Technology,Tianjin 300401,China;Hebei Province Key Laboratory of Big Data Computing(Hebei University of Technology),Tianjin 300401,China;Hebei Data Driven Industrial Intelligent Engineering Research Center(Hebei University of Technology),Tianjin 300401,China)

机构地区：[1]河北工业大学人工智能与数据科学学院,天津300401 [2]河北省大数据计算重点实验室(河北工业大学),天津300401 [3]河北省数据驱动工业智能工程研究中心(河北工业大学),天津300401

出　　处：《计算机应用》2024年第10期3039-3046,共8页journal of Computer Applications

基　　金：河北省自然科学基金资助项目(F2020202028)。

摘　　要：针对知识追踪(KT)模型没有充分考虑学生间差异、挖掘知识状态与习题的高度匹配等问题,提出一种双层网络架构——基于个性化学习和深层次细化的知识追踪(PLDRKT)。首先,利用增强注意力机制得到习题的深层次细化表示;其次,从不同学生对习题的难度感知和学习收益方面对初步知识状态进行个性化建模;最后,利用初步知识状态和深层习题表示得到学生的深层次知识状态并预测他们的未来答题情况。将PLDRKT模型与基于对抗训练的增强知识追踪(ATKT)和集成知识追踪(ENKT)等7种模型在Statics2011、ASSIST09、ASSIST15和ASSIST17数据集上进行对比实验。实验结果显示,PLDRKT模型的曲线下面积(AUC)均有增加,在4个数据集上与不考虑习题嵌入的最优基线模型相比,分别增加了0.61、1.32、5.29和0.19个百分点,可见PLDRKT模型可以较好地建模学生知识状态并预测回答。In response to the problems that Knowledge Tracing(KT)models do not consider differences between students and explore the high matching between knowledge states and exercises,a two-layer network architecture was proposed—Knowledge Tracing based on Personalized Learning and Deep Refinement(PLDRKT).Firstly,the attention enhancement mechanism was used to obtain a deep refined representation of the exercises.Then,personalized modeling of the initial knowledge state was conducted from the perspectives of different students’perceptions of difficulty and learning benefits of the exercises.Finally,the initial knowledge states and the deep exercise representations were used to obtain the students’deep knowledge states and predict their future answering conditions.Comparative experiments were conducted on Statics2011,ASSIST09,ASSIST15,and ASSIST17 datasets among PLDRKT model and seven models such as enhancing Adversarial Training based Knowledge Tracing(ATKT)and ENsemble Knowledge Tracing(ENKT).Experimental results show that the Area Under the Curve(AUC)of PLDRKT model is increased by 0.61,1.32,5.29,and 0.19 percentage points,respectively,compared to the optimal baseline models without considering exercise embedding on four datasets.It can be seen that PLDRKT model can model students’knowledge states and predict answers effectively.

关 键 词：知识追踪 注意力 深层次细化 高度匹配 个性化 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]