Porównaj metody

	Bayesian Knowledge Tracing	Cognitive Diagnostic Modeling	Educational Data Mining
Dziedzina	Education	Education	Education
Rodzina	Machine learning	Latent structure	Machine learning
Rok powstania	1994	2010	2009
Twórca	Albert Corbett & John Anderson	Tatsuoka; DiBello, Roussos & Stout; Junker & Sijtsma; de la Torre	Educational data mining community (Baker, Yacef, Romero, Ventura)
Typ	Two-state hidden Markov model of latent skill mastery from response sequences	Restricted latent class models for diagnosing mastery of discrete skills	Application of data-mining and machine-learning methods to educational data
Źródło pierwotne	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.	Rupp, A. A., Templin, J., & Henson, R. A. (2010). Diagnostic Measurement: Theory, Methods, and Applications. Guilford Press. ISBN: 9781606235270	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.
Inne nazwy	BKT, Knowledge Tracing (Corbett-Anderson), Hidden Markov Knowledge Tracing, Skill Mastery Tracing	CDM, Diagnostic Classification Models, DCM, DINA / G-DINA Models	EDM, Mining Education Data, Data Mining in Education, Learner Data Mining
Pokrewne	3	4	4
Podsumowanie	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.	Cognitive diagnostic models (CDMs), also called diagnostic classification models, are restricted latent class models that report not a single ability score but a profile of which discrete skills or attributes a student has mastered. Each item is linked to the attributes it requires through a Q-matrix, and the model classifies every examinee into one of the possible binary mastery patterns. CDMs answer 'which specific skills does this student lack' rather than 'how much overall ability does this student have,' making them central to fine-grained diagnostic and formative assessment.	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.