The recognition of core knowledge units is helpful for guiding the learners' attention allocation,eliminating disorientation problem.Because these are long-distance dependences on cognition between knowledge units,it is difficult to identify the core knowledge units from a knowledge map by using the traditional centrality indexes,such as degree,closeness,betweenness and eigenvector.An identification method of core knowledge units based on knowledge may topology is proposed,which establishes six-dimensional feature vectors corresponding to knowledge units according to three characteristics like hierarchical distribution,out-degree distribution and Motif structure through the topological analysis of knowledge map.On the basis of six-dimensional feature vectors,the core knowledge unit identification is transformed into a binary classification problem,and a recognition method is implemented by using classification algorithm.With knowledge map dataset of eight courses,a comparative experiment has been conducted with four algorithms,including Support Vector Machine (SVM),Decision Tree (C4.5),Na(i)ve Bayes (NB) and Multi-Layer Perceptron (MLP) and its result demonstrates the effectiveness of the proposed method.%核心知识单元的识别有助于引导学习者的注意力分配,消除学习迷航问题.知识单元之间在认知上具有长距依赖性,常用的度、紧密性、介数、特征向量等中心度指标很难适用于识别知识地图中的核心知识单元.为此,提出了一种基于知识地图拓扑的核心知识单元识别方法.该方法依据对知识地图拓扑分析发现的三个特性,即核心知识单元的层次分布特性、出度分布特性、Motif结构特性,建立了知识单元对应的六维特征向量.在六维特征向量的基础上,将核心知识单元识别问题转化为二类分类问题,采用分类算法实现核心知识单元的识别.在8门课程知识地图数据集上,采用支持向量机(SVM)、决策树C4.5、朴素贝叶斯(NB)和多层感知器(MLP)四种算法进行了对比实验.实验结果表明,所提出的方法有效可行.
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