Concept Map Assessment Through Structure Classification
Fuente:
arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866912300225527808 |
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| author | Vossen, Laís P. V. Gasparini, Isabela Oliveira, Elaine H. T. Czinczel, Berrit Harms, Ute Menzel, Lukas Gombert, Sebastian Neumann, Knut Drachsler, Hendrik |
| author_facet | Vossen, Laís P. V. Gasparini, Isabela Oliveira, Elaine H. T. Czinczel, Berrit Harms, Ute Menzel, Lukas Gombert, Sebastian Neumann, Knut Drachsler, Hendrik |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22741 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Concept Map Assessment Through Structure Classification Vossen, Laís P. V. Gasparini, Isabela Oliveira, Elaine H. T. Czinczel, Berrit Harms, Ute Menzel, Lukas Gombert, Sebastian Neumann, Knut Drachsler, Hendrik Computers and Society Machine Learning 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. |
| title | Concept Map Assessment Through Structure Classification |
| topic | Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2503.22741 |