So sánh phương pháp

	Knowledge Tracing	Mô hình Rasch
Lĩnh vực	Phân tích giáo dục	Trắc lượng tâm lý
Họ	Machine learning	Latent structure
Năm ra đời	1994	1960
Người khởi xướng	Albert Corbett & John Anderson	Georg Rasch
Loại	Probabilistic student modeling	Item Response Theory / Latent trait model
Công trình gốc	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.	Rasch, G. (1960). Probabilistic Models for Some Intelligence and Attainment Tests. Danish Institute for Educational Research, Copenhagen.
Tên gọi khác	BKT, Bayesian Knowledge Tracing, Deep Knowledge Tracing, Bilgi İzleme	1PL IRT, one-parameter logistic model, Rasch Modeli — 1PL IRT, 1PL model
Liên quan	3	6
Tóm tắt	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.	The Rasch model, introduced by Georg Rasch in 1960, is the simplest member of the Item Response Theory (IRT) family. It assigns a single difficulty parameter to each test item and places both item difficulties and person abilities on the same logit scale, enabling direct, sample-independent comparison of items and persons.