Сравнение на методи

	Анализ на обучението	Нелинейно програмиране
Област	Образователна аналитика	Оптимизация
Семейство	Process / pipeline	Process / pipeline
Година на възникване	2011	2006
Създател	George Siemens & Phil Long	Jorge Nocedal & Stephen Wright
Тип	data-driven educational process pipeline	Continuous mathematical optimization
Основополагащ източник	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
Други названия	Educational Data Mining, Academic Analytics, Learning Data Analytics, Öğrenme Analitiği	NLP optimization, Constrained nonlinear optimization, Smooth optimization, Doğrusal olmayan programlama
Свързани	3	3
Резюме	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.