So sánh phương pháp

	Educational Data Mining	Bayesian Knowledge Tracing
Lĩnh vực	Education	Education
Họ	Machine learning	Machine learning
Năm ra đời	2009	1994
Người khởi xướng	Educational data mining community (Baker, Yacef, Romero, Ventura)	Albert Corbett & John Anderson
Loại	Application of data-mining and machine-learning methods to educational data	Two-state hidden Markov model of latent skill mastery from response sequences
Công trình gốc	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.
Tên gọi khác	EDM, Mining Education Data, Data Mining in Education, Learner Data Mining	BKT, Knowledge Tracing (Corbett-Anderson), Hidden Markov Knowledge Tracing, Skill Mastery Tracing
Liên quan	4	3
Tóm tắt	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.	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.