Enhancing Educational Strategy Through K-Means Clustering: A Study on Academic Departments

This study explores the use of k-means clustering for strategic analysis of academic departments within universities, focusing on enrollment and performance metrics. This research aimed at enhancing university administration through data-driven decision making. The research highlights the potential of k-means clustering for resource allocation, curriculum planning, and improving student success. The study categorizes departments into three distinct groups through data collection and cleansing, followed by the application of the Elbow method for optimal cluster determination. These clusters offer insights into departmental dynamics, guiding strategic interventions in areas ranging from high-performance departments to those facing enrollment challenges. The findings underscore the significance of aligning academic offerings with performance excellence, revealing the strategic benefits of a data-informed approach in higher education. This research not only fills a gap in applying quantitative analysis at the departmental level but also opens pathways for further exploration of machine learning in educational strategy, underscoring the transformative potential of analytics in academic management.