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| Analityka uczenia się× | Programowanie nieliniowe× | |
|---|---|---|
| Dziedzina≠ | Analityka edukacyjna | Optymalizacja |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 2011 | 2006 |
| Twórca≠ | George Siemens & Phil Long | Jorge Nocedal & Stephen Wright |
| Typ≠ | data-driven educational process pipeline | Continuous mathematical optimization |
| Źródło pierwotne≠ | Siemens, 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 |
| Inne nazwy | Educational Data Mining, Academic Analytics, Learning Data Analytics, Öğrenme Analitiği | NLP optimization, Constrained nonlinear optimization, Smooth optimization, Doğrusal olmayan programlama |
| Pokrewne | 3 | 3 |
| Podsumowanie≠ | 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. |
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