Comparer des méthodes

	Courbe d'apprentissage (Loi de puissance de la pratique)	Traçage des Connaissances	Analytique de l'apprentissage
Domaine	Analytique de l'éducation	Analytique de l'éducation	Analytique de l'éducation
Famille	Regression model	Machine learning	Process / pipeline
Année d'origine	1936	1994	2011
Auteur d'origine	Theodore Wright	Albert Corbett & John Anderson	George Siemens & Phil Long
Type	Power-law regression model	Probabilistic student modeling	data-driven educational process pipeline
Source fondatrice	Wright, T. P. (1936). Factors affecting the cost of airplanes. Journal of the Aeronautical Sciences, 3(4), 122–128.	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.	Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30–40.
Alias	Power Law of Practice, Experience Curve, Wright's Law, Öğrenme Eğrisi	BKT, Bayesian Knowledge Tracing, Deep Knowledge Tracing, Bilgi İzleme	Educational Data Mining, Academic Analytics, Learning Data Analytics, Öğrenme Analitiği
Apparentées	3	3	3
Résumé	The learning curve models how performance improves predictably as cumulative experience accumulates. Formalized by Theodore Wright in 1936 using aircraft manufacturing data, it expresses the relationship between the number of practice trials (or production units) and the time or cost per unit as a power-law function. It is widely applied in educational psychology, industrial engineering, health professions training, and human factors research whenever repeated task execution is the mechanism of skill acquisition.	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.	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.