Understanding Learning Styles for Adaptive Learning Systems Using K-Means Clustering

Author(s)	Syed Arham Akheel
Country	USA
Abstract	This paper presents an advanced examination of clustering methodologies to enhance adaptive learning systems by leveraging students’ learning styles. Using the ”Students’ Learn- ing Styles Dataset” from the UCI Machine Learning Repository, I employed K-means clustering to stratify students into clusters characterized by distinct engagement metrics, study habits, and demographic factors. These clusters were subsequently analyzed to develop personalized learning pathways, optimized to enhance educational outcomes. The findings reveal that students in high- engagement clusters demonstrate significantly improved perfor- mance when offered customized content. This study underscores the transformative potential of adaptive learning systems in refin- ing educational experiences by accommodating diverse learning styles.
Keywords	Adaptive Learning Systems, K-means Cluster- ing, Learning Styles, Personalized Education, Machine Learning, Unsupervised Learning
Field	Computer > Automation / Robotics
Published In	Volume 1, Issue 3, November-December 2019
Published On	2019-11-26
DOI	https://doi.org/10.36948/ijfmr.2019.v01i03.10061
Short DOI	https://doi.org/g82jb5

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International Journal For Multidisciplinary Research