一种新的自动概念图生成模型C-IK2  

A New Automatic Concept Map Generation Model C-IK2

作　　者：邬宝娴 谢燚 郝天永 沈映姗 WU Baoxian;XIE Yi;HAO Tianyong;SHEN Yingshan(School of Computer Science,South China Normal University,Guangzhou,Guangdong510631,China)

机构地区：[1]华南师范大学计算机学院,广东广州510631

出　　处：《中文信息学报》2023年第11期158-170,共13页Journal of Chinese Information Processing

基　　金：国家社会科学基金(AGA200016)。

摘　　要：概念图能够直观地展示概念间的相互关系,为教师提供概念相关性的建议,因而成为教师进行个性化教学的重要工具。然而,如何生成能反映学生学习能力并有效指导教师教学的概念图是目前概念图研究一大难题。该文提出了一种新的自动概念图生成模型C-IK2。C-IK2模型考虑学生的不同学习特点和不同概念理解程度,通过Birch算法对学生概念掌握程度特征进行聚类处理得到学生分簇。同时该模型考虑概念图具有层次结构,针对传统LPG算法在处理层次结构方面的不足进行了改进。通过融合改进的LPG算法,同时改进K2算法的有效输入序列,最终生成具有不同学生学习特征的层次结构概念图。该文使用两个标准数据集进行实验,与现有基于序列的最新改进K2算法进行对比,C-IK2模型在图准确度上提高了7.7%。与现有基于评分的贝叶斯网络结构学习方法相比,C-IK2模型的图结构质量提高了3.1%。结果表明,C-IK2模型能有效区分学生对概念的理解程度,生成反映理解程度的层次结构概念图,从而帮助教师进行针对性地个性化教学。Concept maps can intuitively display the correlation between concepts and provide teachers with teaching suggestions.Therefore,concept maps have become an important tool for teachers to conduct personalized teaching.However,how to generate a concept map that can reflect students’learning ability and effectively guide teachers’teaching is a big challenge in the current concept map research.This paper proposes a new automatic concept map generation model C-IK2.The C-IK2 model considers students’different learning characteristics and concept understanding levels,and uses Birch algorithm to cluster students’concept mastery characteristics to obtain student clusters.At the same time,the model considers the hierarchical structure of the concept map and is used to guide teachers’teaching,combined with the lack of hierarchical structure of the improved LPG algorithm and the effective input sequence of the improved K2 algorithm to generate hierarchical conceptual maps with different learning characteristics of students.The experiment is based on ASIA standard data,and compared with the existing sequence-based latest improved K2 algorithm,the C-IK2 model improves the accuracy of the graph by 7.7%.Compared with existing score-based Bayesian network structure learning methods,the graph structure quality of the C-IK2 model is improved by 3.1%.Experiments show that the C-IK2 model effectively distinguishes different students’understanding of concepts,and the hierarchical conceptual map generated at the same time has certain effectiveness,thereby helping teachers to carry out personalized teaching.

关 键 词：LPG算法 K2算法 概念图 C-IK2模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]