Abstract

Concept maps have been used to assist learners as an effective learning method in identifying relationships between information, especially when teaching materials have many topics or concepts. However, making a manual concept map is a long and tedious task. It is time-consuming and demands an intensive effort in reading the full content and reasoning the relationships among concepts. Due to this inefficiency, many studies are carried out to develop intelligent algorithms using several data mining techniques. In this research, the authors aim at improving Text Analysis-Association Rules Mining (TA-ARM) algorithm using the weighted K-nearest neighbors (KNN) algorithm instead of the traditional KNN. The weighted KNN is expected to optimize the classification accuracy, which will, eventually, enhance the quality of the generated concept map.

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