摘要
为了客观的评价学生的认知能力,提高整个智能学习系统的智能性,提出了一种采用模糊综合评判和贝叶斯网结合的方法构造学生模型。以离散数学课程为背景,以学生的各个知识点成绩作为学生的行为数据,通过模糊综合评判法将其量化为学生对知识点的掌握情况,将量化结果作为贝叶斯网的输入节点,对学生的下一步行为进行了推理。实验结果表明了该算法的有效性,并得到了良好的效果。
For the objective assessment of the students cognitive competence, improving the intelligence of the learning, a method of building student model is presented based on the combination of fuzzy comprehensive evaluation and Bayesian network. As the background of the course of discrete mathematics and the score of every knowledge point of students is used as their behavioral data. First of all, through fuzzy comprehensive evaluation method quantify the point of knowledge for students to master the situation, and then quantify the results as the input nodes of Bayesian network to reason the students＇ behavior of the next step. The result of the experiment has proved that it verified the validity of the proposed algorithm and obtained good effects.
出处
《计算机工程与设计》
CSCD
北大核心
2009年第10期2554-2557,共4页
Computer Engineering and Design
基金
北京市优秀人才专项基金项目(20071A0501600220)
北京市教育委员会科技发展计划基金项目(KM200610028014)
关键词
学生模型
认知能力
模糊综合评判
贝叶斯网
智能学习
student model
cognitive ability
fuzzy comprehensive evaluation
Bayesian network
intelligent learning
