Transforming Outcome-Based Education with Machine Learning. 1st ed. 2026.

Sonst. Personen	Vetrithangam, D. (Hrsg.); Kumar, Puneet (Hrsg.); Batth, Jaspreet Singh (Hrsg.); Arunadevi, B. (Hrsg.); Saravanan, V. (Hrsg.)
Titel	Transforming Outcome-Based Education with Machine Learning. 1st ed. 2026.
Quelle	Singapore: Springer Nature Singapore (2026), VI, 286 S. PDF als Volltext Link als defekt meldenVerfügbarkeit
Beigaben	Illustrationen 42; farbige Illustrationen 34
Sprache	englisch
Dokumenttyp	online; gedruckt; Monografie
ISBN	978-981-95-4924-5; 978-981-95-4925-2; 978-981-95-4926-9; 978-981-95-4927-6
DOI	10.1007/978-981-95-4925-2
Schlagwörter	Maschinenbau; Technikunterricht; Berufsausbildung; Hochschulbildung; Hochschule
Abstract	This book explores the application of machine learning (ML) in outcome-based education (OBE) and its transformative potential to enhance learning effectiveness. It examines how ML can be seamlessly integrated into various dimensions of OBE to optimize assessment techniques, personalize curriculum design, and modernize teaching methodologies. Emphasizing practical implementation, the book highlights how ML enables personalized learning experiences, supports early intervention strategies, and promotes data-driven decision-making for continuous improvement. Serving as a valuable resource for educators, researchers, and policymakers, it provides actionable insights into leveraging the power of ML to drive innovation and improve educational outcomes. Inhalt: Genesis and growth of outcome based education in higher education -- The future of outcome based education obe leveraging machine learning ml for adaptive curriculum design and real time learning monitoring -- Exploring teachers perceptions of machine learning in outcome based education -- Continuous improvement strategies for success -- Ai and ml for continuous monitoring of cognitive learning -- Integration of machine learning in educational processes -- Predicting student performance with machine learning a crisp dm approach -- Machine learning for improving educational outcomes in primary and secondary schools -- Challenges and future directions transforming obe with machine learning -- Automating curriculum alignment a comparative case study of svm and random forest for co-po mapping in outcome based education -- Advancing outcome based education through machine learning the road ahead -- Ethical considerations and data privacy in ml driven outcome based education.
Erfasst von	Universitätsbibliothek der Humboldt-Universität zu Berlin
Update	2026/4