방법 비교

	Cognitive Diagnostic Modeling	Educational Data Mining	지식 추적
분야	Education	Education	교육 분석학
계열	Latent structure	Machine learning	Machine learning
기원 연도	2010	2009	1994
창시자	Tatsuoka; DiBello, Roussos & Stout; Junker & Sijtsma; de la Torre	Educational data mining community (Baker, Yacef, Romero, Ventura)	Albert Corbett & John Anderson
유형	Restricted latent class models for diagnosing mastery of discrete skills	Application of data-mining and machine-learning methods to educational data	Probabilistic student modeling
원전	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.	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.
별칭	CDM, Diagnostic Classification Models, DCM, DINA / G-DINA Models	EDM, Mining Education Data, Data Mining in Education, Learner Data Mining	BKT, Bayesian Knowledge Tracing, Deep Knowledge Tracing, Bilgi İzleme
관련	4	4	3
요약	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.	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.