Comparer des méthodes

	Théorie des espaces de connaissances	Modèles de diagnostic cognitif (DINA / G-DINA)
Domaine	Analytique de l'éducation	Psychométrie
Famille	Machine learning	Latent structure
Année d'origine	1985	2011
Auteur d'origine	Jean-Paul Doignon & Jean-Claude Falmagne	Jimmy de la Torre
Type	Combinatorial knowledge assessment framework	Latent variable diagnostic classification model
Source fondatrice	Doignon, J.-P., & Falmagne, J.-C. (1985). Spaces for the assessment of knowledge. International Journal of Man-Machine Studies, 23(2), 175–196.	de la Torre, J. (2011). The generalized DINA model framework. Psychometrika, 76(2), 179–199.
Alias	KST, Knowledge Structures, Competence-Based Knowledge Space Theory, Bilgi Uzayı Teorisi	Diagnostic Classification Model, Skills Assessment Model, Attribute Mastery Model, Bilişsel Tanı Modeli
Apparentées	3	2
Résumé	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.	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.