Associated Patterns in Open-Ended Concept Maps within E-Learning

Abstract

A concept map is a diagram that visualizes the structure of individual cognitive knowledge. An approach to creating a concept map structure that allows users to contribute concepts and linkages that express their understanding freely is known as an "open-ended concept map." It has been demonstrated that an open-ended concept map accurately depicts student knowledge structures and reveals student differences. However, manually analyzing an open-ended map is difficult, time-consuming, and includes many propositions, especially in a big classroom. Educational data mining could be used to further process and analyze a collection of concept maps. However, many works attempted to employ data mining in order to produce concept maps structure from text documents rather than examining the knowledge representation. This study aimed to identify hidden students' knowledge representation combination patterns using association rules analysis. The dataset used in this study consisted of 27 open-concept maps created by university students. This study found interesting patterns that reveal students' knowledge in understanding the material given by the teacher.