Unlocking Student Insights: Performance Analysis with Enhanced K-Means

Ajay Sonawane

ISBN 10: 6208172373 / ISBN 13: 9786208172374

Edité par LAP LAMBERT Academic Publishing Okt 2024, 2024

A propos de cet article

This item is printed on demand - it takes 3-4 days longer - Neuware 84 pp. Englisch.

N° de réf. du vendeur 9786208172374

Détails bibliographiques

• Titre: Unlocking Student Insights: Performance Analysis with Enhanced K-Means
• Éditeur: LAP LAMBERT Academic Publishing Okt 2024
• Date de parution: 2024
• Langue: anglais
• ISBN à 10 chiffres: 6208172373
• ISBN à 13 chiffres: 9786208172374
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 220 L x 150 H x 6 P (en millimètres) 220 millimètres de largeur par 150 millimètres de hauteur par 6 millimètres de profondeur
• Poids: 143 grammes

A propos de ce titre

Synopsis

Machine Learning (ML) is a branch of artificial intelligence that allows computers to learn from data without explicit programming. ML is applied in various fields, including education, pattern recognition, industries, social media, and product recommendations. In education, ML and Educational Data Mining (EDM) are becoming crucial due to the growing amount of student data. EDM helps uncover hidden information in educational datasets, aiding in student performance improvement and better decision-making for teachers and institutions. Techniques like clustering (e.g., modified K-means) and classification (e.g., decision trees) are commonly used to analyze student performance. Clustering groups students based on characteristics, while the Elbow method helps determine optimal cluster size. ML in education helps enhance student outcomes and optimize administrative decisions, making it valuable for both students and institutions. This data-driven approach is key to improving education quality in the future.