So sánh phương pháp

	Lý thuyết Không gian Tri thức	Mô hình Chẩn đoán Nhận thức (DINA / G-DINA)	Knowledge Tracing
Lĩnh vực	Phân tích giáo dục	Trắc lượng tâm lý	Phân tích giáo dục
Họ	Machine learning	Latent structure	Machine learning
Năm ra đời	1985	2011	1994
Người khởi xướng	Jean-Paul Doignon & Jean-Claude Falmagne	Jimmy de la Torre	Albert Corbett & John Anderson
Loại	Combinatorial knowledge assessment framework	Latent variable diagnostic classification model	Probabilistic student modeling
Công trình gốc	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.	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.
Tên gọi khác	KST, Knowledge Structures, Competence-Based Knowledge Space Theory, Bilgi Uzayı Teorisi	Diagnostic Classification Model, Skills Assessment Model, Attribute Mastery Model, Bilişsel Tanı Modeli	BKT, Bayesian Knowledge Tracing, Deep Knowledge Tracing, Bilgi İzleme
Liên quan	3	2	3
Tóm tắt	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.	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.