融合行为和遗忘因素的贝叶斯知识追踪模型研究  

Research on Bayesian knowledge tracking model integrating behavior andforgetting factors

作　　者：黄诗雯 刘朝晖[1,2] 罗凌云[1] 赵忠源 王璨 Huang Shiwen;Liu Zhaohui;Luo Lingyun;Zhao Zhongyuan;Wang Can(School of Computer Science,University of South China,Hengyang Hunan 421001,China;School of Innovation&Entrepreneurship,University of South China,Hengyang Hunan 421001,China)

机构地区：[1]南华大学计算机学院,湖南衡阳421001 [2]南华大学创新创业学院,湖南衡阳421001

出　　处：《计算机应用研究》2021年第7期1993-1997,共5页Application Research of Computers

基　　金：湖南省教育厅基金资助项目(18C0413);南华大学学位与研究生教育教改课题(2017JG014);2020年湖南省普通高等学校教学改革研究项目(HNJG-2020-0477)。

摘　　要：贝叶斯知识追踪模型(Bayesian knowledge tracing,BKT)被用于智能教学系统中追踪学习者的知识状态并预测其掌握水平和未来表现。由于BKT容易忽视记忆遗忘现象,以及未考虑学习行为对表现结果产生的影响,导致模型预测结果与实际情况出现偏差。针对此问题,提出了一种融合学习者的行为和遗忘因素的贝叶斯知识追踪模型(behavior-forgetting Bayesian knowledge tracing,BF-BKT)。首先,采用决策树算法处理学习行为数据,引入行为节点;然后初始化遗忘参数并赋值,更新学习者知识掌握水平的算法;最后,利用ASSISTMENTS提供的公开数据集对相关模型的预测精度进行对比。实验验证,BF-BKT能够达到更好的预测精度。BKT can track the state of the learners’knowledge,as well as predict their mastery level and future performance in the intelligent teaching system.Because BKT easily ignores the phenomenon of memory forgetting,and does not consider the impact of learning behavior on performance results,the model prediction results deviate from the actual situation.So this paper proposed a new Bayesian knowledge tracking model that integrated learner behavior and forgetting factors.Firstly,the method used the decision tree algorithm to process learning behavior data and introduced behavior nodes.Then,the model initialized the forgetting parameters and assigned them,and updated the algorithm of the learner’s knowledge mastery level.Finally,the experiment compared the prediction accuracy of related models through the public data set provided by ASSISTMENTS.Experimental results show that the BF-BKT model can achieve better prediction accuracy.

关 键 词：贝叶斯网络 知识追踪 学习行为 记忆遗忘 预测精度 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]