Bandingkan kaedah

	Analitik Pembelajaran	Pengaturcaraan Tak Linear
Bidang	Analitik Pendidikan	Pengoptimuman
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	2011	2006
Pengasas	George Siemens & Phil Long	Jorge Nocedal & Stephen Wright
Jenis	data-driven educational process pipeline	Continuous mathematical optimization
Sumber perintis	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
Alias	Educational Data Mining, Academic Analytics, Learning Data Analytics, Öğrenme Analitiği	NLP optimization, Constrained nonlinear optimization, Smooth optimization, Doğrusal olmayan programlama
Berkaitan	3	3
Ringkasan	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.