Sammenlign metoder

	Kognitive diagnosemodeller (DINA / G-DINA)	Kunnskapssporing
Fagfelt	Psykometri	Utdanningsanalyse
Familie	Latent structure	Machine learning
Opprinnelsesår	2011	1994
Opphavsperson	Jimmy de la Torre	Albert Corbett & John Anderson
Type	Latent variable diagnostic classification model	Probabilistic student modeling
Opprinnelig kilde	de la Torre, J. (2011). The generalized DINA model framework. Psychometrika, 76(2), 179–199.	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.
Alias	Diagnostic Classification Model, Skills Assessment Model, Attribute Mastery Model, Bilişsel Tanı Modeli	BKT, Bayesian Knowledge Tracing, Deep Knowledge Tracing, Bilgi İzleme
Relaterte	2	3
Sammendrag	Cognitive Diagnosis Models (CDMs) are a family of latent variable models designed to classify examinees according to their mastery of a set of discrete cognitive attributes or skills. The Generalized DINA (G-DINA) framework, introduced by Jimmy de la Torre in 2011, provides a unifying structure that encompasses many specific CDMs — including the DINA, DINO, ACDM, and LLM models — as special cases, enabling fine-grained diagnostic feedback beyond a single total score.	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.