Vertaile menetelmiä

	Oppimisanalytiikka	Tietotila-teoria
Tieteenala	Koulutusanalytiikka	Koulutusanalytiikka
Menetelmäperhe	Process / pipeline	Machine learning
Syntyvuosi	2011	1985
Kehittäjä	George Siemens & Phil Long	Jean-Paul Doignon & Jean-Claude Falmagne
Tyyppi	data-driven educational process pipeline	Combinatorial knowledge assessment framework
Alkuperäislähde	Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30–40.	Doignon, J.-P., & Falmagne, J.-C. (1985). Spaces for the assessment of knowledge. International Journal of Man-Machine Studies, 23(2), 175–196.
Rinnakkaisnimet	Educational Data Mining, Academic Analytics, Learning Data Analytics, Öğrenme Analitiği	KST, Knowledge Structures, Competence-Based Knowledge Space Theory, Bilgi Uzayı Teorisi
Liittyvät	3	3
Tiivistelmä	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.	Knowledge Space Theory (KST) is a combinatorial, set-theoretic framework for modeling and assessing human knowledge, introduced by Jean-Paul Doignon and Jean-Claude Falmagne in 1985. It represents a learner's competence as a subset of a problem domain, organizes all feasible competence subsets into a lattice called a knowledge space, and uses probabilistic inference to locate a learner within that space. The approach underlies adaptive testing and intelligent tutoring systems, offering a mathematically rigorous alternative to classical test theory.