Clustering Of Informatics Students Based On Understanding The Material Using The K-Means Method

Abstract

The level of student understanding in coursework is a crucial determinant of academic success, reflecting both teaching quality and the effectiveness of applied learning methods. In the context of Informatics, challenges often stem from the complexity of subjects such as algorithms, programming, and data analysis, which require analytical and in-depth comprehension. However, differences in learning abilities, backgrounds, and styles often result in varying levels of understanding among students. This study investigates the application of k-means clustering as an innovative method to analyze academic data and classify students based on their understanding of course materials. By utilizing data such as exam scores, quiz results, and classroom engagement, k-means clustering identifies patterns in students’ comprehension levels, offering educators insights to tailor teaching strategies effectively. The findings of this study are expected to aid educators in designing targeted interventions, enhance learning processes, and support an inclusive and effective academic environment.