Unlocking Student Insights: Performance Analysis with Enhanced K-Means

Ajay Sonawane

ISBN 10: 6208172373 / ISBN 13: 9786208172374

Editore: LAP LAMBERT Academic Publishing Okt 2024, 2024

Riguardo questo articolo

Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 84 pp. Englisch.

Codice articolo 9786208172374

Dati bibliografici

• Titolo: Unlocking Student Insights: Performance Analysis with Enhanced K-Means
• Editore: LAP LAMBERT Academic Publishing Okt 2024
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 6208172373
• ISBN 13: 9786208172374
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 220 L x 150 A x 6 P (millimetri) 220 millimetri di larghezza per 150 millimetri di altezza per 6 millimetri di profondità
• Peso: 143 grammi

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Riassunto

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.