通过结构分类提升概念图评估精度，实现86%准确率。
Concept Map Assessment Through Structure Classification
- 基于三种结构类型（辐条、网络、链式）进行概念图分类。
- 利用317个概念图数据训练决策树模型，准确率达86%。
- 适合教育技术系统开发者用于实时反馈学生知识结构。
由于其多功能性，概念图被广泛应用于各类教育场景中，是帮助教师理解学生知识建构的有效工具。分析概念图的关键在于其结构，可划分为三种类型：辐条型、网络型和链式型。识别主要结构有助于判断学生对主题的理解深度。本研究分析了317个不同概念图结构，将其分类为上述三类，并利用地图的统计与描述性信息训练多类分类模型。结果表明，采用决策树方法可实现86%的分类准确率。该成果可用于概念图评估系统，为学生提供实时反馈。
原文摘要 · Abstract (English)
Due to their versatility, concept maps are used in various educational settings and serve as tools that enable educators to comprehend students' knowledge construction. An essential component for analyzing a concept map is its structure, which can be categorized into three distinct types: spoke, network, and chain. Understanding the predominant structure in a map offers insights into the student's depth of comprehension of the subject. Therefore, this study examined 317 distinct concept map structures, classifying them into one of the three types, and used statistical and descriptive information from the maps to train multiclass classification models. As a result, we achieved an 86\% accuracy in classification using a Decision Tree. This promising outcome can be employed in concept map assessment systems to provide real-time feedback to the student.
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