Сравнение методов

	Educational Data Mining	Трассировка знаний
Область	Education	Образовательная аналитика
Семейство	Machine learning	Machine learning
Год появления	2009	1994
Автор метода	Educational data mining community (Baker, Yacef, Romero, Ventura)	Albert Corbett & John Anderson
Тип	Application of data-mining and machine-learning methods to educational data	Probabilistic student modeling
Основополагающий источник	Baker, R. S. J. d., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3–17.	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.
Другие названия	EDM, Mining Education Data, Data Mining in Education, Learner Data Mining	BKT, Bayesian Knowledge Tracing, Deep Knowledge Tracing, Bilgi İzleme
Связанные	4	3
Сводка	Educational data mining (EDM) is the field that develops and applies data-mining and machine-learning methods to data generated by educational settings — clickstreams from online courses, intelligent tutoring system logs, assessment records, and student information systems. Its goal is to discover patterns that explain and predict learning: who is at risk of failing, how students work through material, which content sequences help, and what hidden skill structures underlie performance. EDM treats fine-grained learner data as a source of actionable scientific and practical insight.	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.