<![CDATA[The study aims to analyze students’ mathematics learning styles based on the clusters formed using the K-Means Clustering algorithm and to identify differences in mathematics learning outcomes in each cluster. This study employed a quantitative method with an exploratory approach. The data used were primary data obtained through learning style questionnaires and mathematics learning outcome tests administered to 129 tenth grade students. The number of cluster was determined as three clusters based on the theory of visual (cluster 3), auditory (cluster 2), and kinesthetic (cluster 1) learning styles. The analysis of mathematics learning outcomes revealed that the highest average score was found in cluster 3 (visual), while the lowest average score was found in cluster 2 (auditory). The findings indicate that the K-Means Clustering Algorithm can be used to identify students’ dominant learning styles, allowing students to recognize, understand, and develop awareness of their learning preferences, which may support more effective learning processes.]]>
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