关键词: 教育数据挖掘, 知识点标注, 二部图

Abstract: Aiming at the problem of knowledge points labeling for test questions in online education, a bipartite graph-based method is proposed for knowledge points labeling in this paper. Firstly, considering the problem that the granularity of knowledge points is fuzzy, the knowledge graph of knowledge points is constructed to integrate knowledge points. Secondly, based on the corpus of textbooks and test questions, we extract the knowledge points bipartite graph as well as the test questions bipartite graph, calculate the weights of edges by the TF-IDF to construct a bipartite graph model between knowledge points and test questions. Besides, similarity measurement method based on term frequency weighting is used to calculate the similarity between questions and knowledge points, marking the knowledge points with the highest similarity to the test questions. Finally, the high school history test questions on the online education platform are used as the experimental data sets.The experiments and analysis show that the proposed method is obviously superior to classical machine learning methods such as Naive Bayes, K-Nearest Neighbor, Random Forest and Support Vector Machines.

Key words: educational data mining, knowledge points labeling, bipartite graph