面向虚拟学习社区的学习行为特征挖掘与分组方法的研究

虚拟学习社区作为网络教育的一种新兴模式,越来越受到人们的关注.如何为不同的学习者提供良好的个性化教学服务是虚拟学习社区研究的重要问题,而学习者的学习特征的提取与分析是个性化教学的基础.以虚拟学习社区为背景,从学习者的学习过程和学习特征入手,运用模糊c均值聚类算法(FCM)挖掘并分析学习过程中学习者的学习特征,进而根据学习者的知识水平、自主学习和协作学习积极性等学习特征进行精确分组划分,以达有针对性的教学指导,实现个性化教学,提高学习者学习效率.

Learning Behavior Feature Mining and Grouping Method for Virtual Learning Community

Virtual learning community,as a new mode of network education,has attracted more and more attention.How to provide a good teaching service for different learners is an important issue in the virtual learning community,and the extraction and analysis of the characteristics of the learners are the basis of the individualized teaching.It starts from the learner’s learning process and learning characteristics,using the fuzzy c-means clustering algorithm(FCM),mining and analyzing the learning features of students in the learning process in the background of the virtual learning community.Students are accurate grouped according to the learner’s knowledge level,autonomous learning and cooperative learning initiative and other learning characteristics in order to achieve the goal of personalized teaching,to improve the learning efficiency of the learners.Key words:virtual learning community; fuzzy c-means clustering algorithm(FCM),mining and analyzing the learning features of students in the learning process in the background of the virtual learning community.Students are accurate grouped according to the learner’s knowledge level,autonomous learning and cooperative learning initiative and other learning characteristics in order to achieve the goal of personalized teaching,to improve the learning efficiency of the learners.