Vertaile menetelmiä

	Kognitiiviset diagnoosimallit (DINA / G-DINA)	Formaali konseptianalyysi (FCA)	Knowledge Tracing
Tieteenala	Psykometriikka	Pehmeä laskenta	Koulutusanalytiikka
Menetelmäperhe	Latent structure	Machine learning	Machine learning
Syntyvuosi	2011	1982	1994
Kehittäjä	Jimmy de la Torre	Rudolf Wille & Bernhard Ganter	Albert Corbett & John Anderson
Tyyppi	Latent variable diagnostic classification model	Lattice-based knowledge representation / concept mining	Probabilistic student modeling
Alkuperäislähde	de la Torre, J. (2011). The generalized DINA model framework. Psychometrika, 76(2), 179–199.	Wille, R. (1982). Restructuring lattice theory: an approach based on hierarchies of concepts. In I. Rival (Ed.), Ordered Sets (pp. 445–470). Reidel.	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.
Rinnakkaisnimet	Diagnostic Classification Model, Skills Assessment Model, Attribute Mastery Model, Bilişsel Tanı Modeli	FCA, concept lattice analysis, Galois lattice, biçimsel kavram analizi	BKT, Bayesian Knowledge Tracing, Deep Knowledge Tracing, Bilgi İzleme
Liittyvät	2	3	3
Tiivistelmä	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.	Formal concept analysis derives a hierarchy of concepts from a simple table of which objects have which attributes. Founded by Rudolf Wille in 1982 on lattice theory, it pairs each set of objects with the attributes they all share to form 'formal concepts', then organizes these into a concept lattice — a mathematically grounded, interpretable hierarchy used for knowledge discovery, ontology building, and explainable analysis of categorical data.	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.