Bayesian Network with Grey Entropy Data Pre-Processing for Modeling Students Learning Status

摘要：

In this paper, our study aimed to use Grey entropy to help decide which attributes, so called items in educational assessment, should be eliminated to prevent the Bayesian network modeling process from over-fitting and to obtain better accuracy. Although Bayesian network is proving to be the best technology available for diagnosing students’ learning status in educational assessment, in the process of constructing a Bayesian network, the criteria of selecting testing attributes such as items or tasks will influence the diagnosing accuracy. Experiment results indicats that the Bayesian network with Grey entropy data pre-processing obtains the better more than 10% in accuracy than the man-made Bayesian network.

作者: Tien-Yu Hsieh Bor-Chen Kuo Rih-Chang Chao Shin-I Yeh Pei-Chieh Chen

作者单位: Graduate Institute of Educational Measurement and Statistics, National Taichung University, Taichung Education Department Chiayi County Government,Chiayi, Taiwan Graduate Institute of Elementary and Secondary Education, National Chiayi University, Chiayi, Taiwan

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Grey System and Intelligent Services(2009 IEEE灰色系统与服务科学国际会议)

会议地点: 南京

会议语种:英文

页码: 502-505

在线出版日期: 2009-10-20（万方平台首次上网日期，不代表论文的发表时间）