Concept Map Assessment Through Structure Classification
This work addresses the need for real-time feedback in educational assessment systems, though it is incremental as it applies existing classification methods to concept map data.
The study tackled the problem of automatically classifying concept map structures into spoke, network, or chain types to assess student comprehension, achieving an 86% accuracy using a Decision Tree model.
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.