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	Bayesian Knowledge Tracing	Educational Data Mining	Knowledge Tracing
Campo	Education	Education	Analisi dei dati educativi
Famiglia	Machine learning	Machine learning	Machine learning
Anno di origine	1994	2009	1994
Ideatore	Albert Corbett & John Anderson	Educational data mining community (Baker, Yacef, Romero, Ventura)	Albert Corbett & John Anderson
Tipo	Two-state hidden Markov model of latent skill mastery from response sequences	Application of data-mining and machine-learning methods to educational data	Probabilistic student modeling
Fonte seminale	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.	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.
Alias	BKT, Knowledge Tracing (Corbett-Anderson), Hidden Markov Knowledge Tracing, Skill Mastery Tracing	EDM, Mining Education Data, Data Mining in Education, Learner Data Mining	BKT, Bayesian Knowledge Tracing, Deep Knowledge Tracing, Bilgi İzleme
Correlati	3	4	3
Sintesi	Bayesian knowledge tracing (BKT) is a model that estimates, after each problem a student attempts, the probability that the student has mastered the underlying skill. Introduced by Corbett and Anderson for intelligent tutoring systems, it is a two-state hidden Markov model: the latent variable is whether the skill is learned or not, and observed correct/incorrect responses update that latent state through Bayesian inference. With just four parameters — initial knowledge, learning, slip, and guess — BKT drives the mastery decisions that tell a tutor when a student can move on.	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.