基于贝叶斯网络的学习者学习行为评估算法研究

Research on Evaluation Algorithm of Learner's Learning Behavior Based on Bayesian Network

针对现有大部分教学平台提供给学习者相同的学习资源,无法对学习者的学习情况、知识点掌握情况进行实时评测和评估的情况,提出基于贝叶斯网络的学习者学习行为评估模型,主要针对学习者在教学系统中的解题过程行为进行评估及预测.文中首先提出的解决方法是对于每一类题目根据学习者的解题过程分别构建贝叶斯网络模型,但这种方法也存在着一定的不足之处.在此基础上,根据题目之间的关联性进而提出链式贝叶斯网络模型.最后采用KDD 2010比赛中提供的智能辅导教学系统学生日志数据分别验证这两种方法的可行性及有效性.

Most of the existing teaching platforms which provide the same learning resources for the learners can't evaluate the learning situation and the mastery of knowledge of the learners in real time. According to this situation,a learner?s learning behavior evaluation model based on Bayesian network which mainly evaluates and predicts learnersy behavior in solving problems in Tutoring System was proposed. The first solution is to construct a Bayesian network model for each problem according to the learners? problem-solving process,but this solution has some shortcomings丒 On this basis,a chain Bayesian network model was proposed according to the relevance between problems丒 Finally, support for feasibility and validity of these two solutions was provided by applying them to logs of student interaction with Intelligent Tutoring System from the KDD Cup 2010.