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	Analityka uczenia się	Knowledge Tracing
Dziedzina	Analityka edukacyjna	Analityka edukacyjna
Rodzina	Process / pipeline	Machine learning
Rok powstania	2011	1994
Twórca	George Siemens & Phil Long	Albert Corbett & John Anderson
Typ	data-driven educational process pipeline	Probabilistic student modeling
Źródło pierwotne	Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30–40.	Corbett, A. T., & Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253–278.
Inne nazwy	Educational Data Mining, Academic Analytics, Learning Data Analytics, Öğrenme Analitiği	BKT, Bayesian Knowledge Tracing, Deep Knowledge Tracing, Bilgi İzleme
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.	Knowledge Tracing (KT) is a student-modeling technique that estimates, at each moment in time, the probability that a learner has mastered a target knowledge component. Introduced by Corbett and Anderson in 1994, the classical Bayesian Knowledge Tracing (BKT) model treats skill acquisition as a two-state Hidden Markov Model driven by four interpretable parameters: prior knowledge, learning rate, slip, and guess. Deep variants (DKT, DKVMN, AKT) later replaced HMMs with recurrent and transformer architectures.