Сравнение методов

	Трассировка знаний	Байесовская сеть
Область	Образовательная аналитика	Байесовские методы
Семейство	Machine learning	Bayesian methods
Год появления	1994	1988
Автор метода	Albert Corbett & John Anderson	Judea Pearl
Тип	Probabilistic student modeling	Probabilistic graphical model
Основополагающий источник	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.	Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797
Другие названия	BKT, Bayesian Knowledge Tracing, Deep Knowledge Tracing, Bilgi İzleme	Bayes network, belief network, probabilistic graphical model, directed graphical model
Связанные	3	4
Сводка	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.	A Bayesian network is a probabilistic graphical model, introduced by Judea Pearl in 1988, that encodes a set of variables and their conditional dependencies as a directed acyclic graph (DAG). Each node represents a variable; each directed edge encodes a direct probabilistic influence. By combining Bayes' rule with the graph's conditional independence structure, the model supports reasoning under uncertainty — computing the probability of any variable given observed evidence about others.