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Exploring Leadership Attributes in High-Performing Educational Institutions
Abstract
Data analysis techniques in educational leadership play a crucial role in enhancing the effectiveness and decision-making capabilities of educational institutions. This study explores the application of advanced data analysis methods, including machine learning and computer vision, to assess leadership attributes and competencies in educational leaders. Specifically, the research focuses on utilizing convolutional neural networks, such as ResNet50, to analyze visual data (e.g., facial expressions, body language, and gestures) as indicators of managerial competence and leadership qualities. By examining these visual cues, the study aims to uncover deeper insights into the traits that distinguish high-performing leaders in educational settings. Through feature extraction and selection techniques like PCA (Principal Component Analysis) and PSO (Particle Swarm Optimization), the study refines the data to highlight relevant leadership traits.
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