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Learning Analytics×Nelineární programování×
OborAnalytika ve vzděláváníOptimalizace
RodinaProcess / pipelineProcess / pipeline
Rok vzniku20112006
TvůrceGeorge Siemens & Phil LongJorge Nocedal & Stephen Wright
Typdata-driven educational process pipelineContinuous mathematical optimization
Původní zdrojSiemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30–40. link ↗Nocedal, J., & Wright, S. J. (2006). Numerical Optimization (2nd ed.). Springer. ISBN: 978-0-387-30303-1
Další názvyEducational Data Mining, Academic Analytics, Learning Data Analytics, Öğrenme AnalitiğiNLP optimization, Constrained nonlinear optimization, Smooth optimization, Doğrusal olmayan programlama
Příbuzné33
ShrnutíLearning 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.
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ScholarGatePorovnat metody: Learning Analytics · Nonlinear Programming. Získáno 2026-06-17 z https://scholargate.app/cs/compare