विधियों की तुलना करें

	लर्निंग एनालिटिक्स	अरेखीय प्रोग्रामन
क्षेत्र	शिक्षा विश्लेषिकी	अनुकूलन
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	2011	2006
प्रवर्तक	George Siemens & Phil Long	Jorge Nocedal & Stephen Wright
प्रकार	data-driven educational process pipeline	Continuous mathematical optimization
मौलिक स्रोत	Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30–40.	Nocedal, J., & Wright, S. J. (2006). Numerical Optimization (2nd ed.). Springer. ISBN: 978-0-387-30303-1
उपनाम	Educational Data Mining, Academic Analytics, Learning Data Analytics, Öğrenme Analitiği	NLP optimization, Constrained nonlinear optimization, Smooth optimization, Doğrusal olmayan programlama
संबंधित	3	3
सारांश	Learning Analytics is the measurement, collection, analysis, and reporting of data about learners and their contexts, with the purpose of understanding and optimizing learning and the environments in which it occurs. Formally introduced by George Siemens and Phil Long in 2011, the approach draws on data generated in digital learning environments to provide educators, institutions, and learners with evidence-based feedback for improving educational outcomes.	Nonlinear programming (NLP) is a branch of mathematical optimization concerned with problems in which the objective function or at least one constraint is nonlinear. Formalized comprehensively by Jorge Nocedal and Stephen Wright in their seminal 2006 text, NLP encompasses gradient-based algorithms — including sequential quadratic programming (SQP), interior-point methods, and quasi-Newton approaches — for finding locally or globally optimal solutions to continuous decision problems arising across engineering, economics, and the physical sciences.