K-Means Clustering in Jamovi | Student Performance Dataset

This video demonstrates the application of K-Means Clustering in Jamovi to analyze a Student Performance Dataset. The objective is to group students into distinct clusters based on their academic performance and related characteristics. Through unsupervised machine learning techniques, patterns and similarities among students are identified without predefined labels. The analysis includes data preparation, cluster formation, interpretation of cluster characteristics, and visualization of results. This approach helps educators and researchers understand student segments and make data-driven decisions for improving academic outcomes.