基于关联模式挖掘和聚类的课程知识重难点分析

Analysis of Key and Difficult Points of Course Knowledge Based on Association Pattern Mining and Clustering

对课堂教学场景中课程内容的重难点进行深入探索和分析,不仅可以提升教师的授课效率,还可以为学生提供满足其学习需求的资源。然而,教育数据挖掘(educational data mining,EDM)中以课程知识点为对象的挖掘方法仍有待进一步研究。为能够得到课程内知识点间关联并明确知识点难度,依据结果明确重点知识和难点知识,提出一种面向课程知识的重难点分析方法。其中,基于关联模式挖掘方法获取课程知识点间的关联,进而明确重点知识;基于聚类对知识点、学生分类分析,进而明确难点知识。在某校程序设计课程场景进行了实验,验证了所提出方法的可行性。

In the course teaching scene,deeply explore and analyze of the important and difficult course content can not only improve the teaching efficiency of teachers but also provide students with resources that meet their learning needs.However,in Educational Data Mining(EDM),the mining method for curriculum knowledge points remains to be further studied.In order to get the correlation of knowledge points,the difficulty of knowledge points,and to make clear the key,difficult knowledge in a course according to the analysis results,propose an analysis method for course knowledge points that can infer the important and difficult knowledge.In the process,use the association pattern mining method to obtain the association between the course knowledge points,so as to clarify the key and difficult points of course knowledge based on association pattern mining and clustering;use the clustering method to classify and analyze the knowledge points and students,so as to clarify the difficult knowledge.Experiments on a real-world programming course dataset of a school verify the feasibility of proposed method.