学习者知识模型的在线学习算法

doi:10.3724/SP.J.1087.2012.00436

计算机应用 ›› 2012, Vol. 32 ›› Issue (02): 436-439.DOI: 10.3724/SP.J.1087.2012.00436

学习者知识模型的在线学习算法

李唯实¹,毛晓光¹,谢建文²

1. 国防科学技术大学计算机学院，长沙 410073
2. 北京国之源软件技术有限公司湖南研发基地，长沙 410003

收稿日期:2011-07-08 修回日期:2011-09-10 发布日期:2012-02-23 出版日期:2012-02-01
通讯作者: 李唯实
作者简介:李唯实(1982-)，男，湖南隆回人，助理工程师，硕士，主要研究方向：自主学习、学习者模型；
毛晓光(1970-)，男，浙江江山人，教授，博士生导师，博士，主要研究方向：高可信软件、软件开发、软件支持；
谢建文(1974－)，男，湖南长沙人，讲师，硕士，主要研究方向：基础教育信息化。

Online learning algorithm for learner knowledge model

LI Wei-shi¹,MAO Xiao-guang¹,XIE Jian-wen²

1. School of Computer Science, National University of Defense Technology, Changsha Hunan 410073, China
2. Hunan Research and Development Base, Beijing ChinaSchool Resource Software Research and Development Company Limited, Changsha Hunan 410003, China

Received:2011-07-08 Revised:2011-09-10 Online:2012-02-23 Published:2012-02-01
Contact: LI Wei-shi

摘要/Abstract

摘要： 学习者知识模型是智能授导系统(ITS)中教学过程实现和策略实施的基础，然而由于判别学习者知识掌握程度的不确定性和学习者知识掌握水平的实时变化，构建能正确反映学习者知识掌握程度及其变化的知识模型十分困难。基于贝叶斯网络，以知识项为基本节点构建学习者知识模型的结构；引入问题节点，根据学习者的学习测试结果，采用Voting EM算法来对知识模型的参数进行在线学习和更新；同时，通过设置置信因子和更新时间标记来改进在线学习的效果。实验表明，模型能够较好地反映学习者知识掌握状况和快速适应学习者知识掌握水平的变化，有助于ITS更好地评价学习者学习效果。

关键词: 知识模型, 贝叶斯网络, 在线学习, 置信因子

Abstract: Learner knowledge model is the foundation of the teaching process and strategy on Intelligent Tutoring Systems (ITS). Because of the uncertainty of recognizing knowledge level of learner and the real-time changes of knowledge level, it is very difficult to construct a model to reflect learner's knowledge level and its change correctly. The paper used Bayesian network for learner knowledge modeling. According to knowledge level's change during learners' learning process, problem knot was introduced into knowledge model, and Voting EM algorithm was used for online learning and updating of knowledge model's parameters. Finally, the paper introduced confidence factor and time updating mark to improve the efficiency of online parameters learning and revise the result. The experimental results indicate that the model can reflect learner's knowledge status better, and can quickly keep up with the change of knowledge level. It can help ITS to evaluate the learning effects better.

Key words: knowledge model, Bayesian network, online learning, confidence factor

中图分类号:

TP182

李唯实毛晓光谢建文. 学习者知识模型的在线学习算法[J]. 计算机应用, 2012, 32(02): 436-439.

LI Wei-shi MAO Xiao-guang XIE Jian-wen. Online learning algorithm for learner knowledge model[J]. Journal of Computer Applications, 2012, 32(02): 436-439.

参考文献

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学习者知识模型的在线学习算法

Online learning algorithm for learner knowledge model

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