Concept Map Assessment Through Structure Classification

2025-03-26

AI Summary

This study addresses the automated assessment of concept maps in educational settings, aiming to characterize the depth of students’ knowledge construction through structural typology classification—specifically, hub-and-spoke, chain, and network structures. We propose the first systematic, computationally grounded definition and quantification of these three structural types, integrating graph-theoretic features (e.g., node degree, average path length, number of connected components) with pedagogically interpretable descriptive metrics. A decision-tree-based multiclass supervised classification model is developed and evaluated on a real-world dataset of 317 student-generated concept maps, achieving 86% classification accuracy. Results demonstrate that structural typology serves as a valid proxy for assessing conceptual understanding. Our primary contributions are: (1) establishing the first formal, computable framework for concept map structural classification; (2) balancing high discriminative performance with educational interpretability; and (3) enabling real-time formative feedback and data-informed instructional interventions.

Technology Category

• Machine Learning: Structured Learning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

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.

Problem

• Classify concept map structures into spoke, network, or chain types
• Analyze student comprehension depth via concept map structure classification
• Develop real-time feedback systems for concept map assessment

Innovation

• Classified concept maps into three structure types
• Used statistical data for multiclass classification models
• Achieved 86% accuracy with Decision Tree