เปรียบเทียบวิธี

	Educational Data Mining	Bayesian Knowledge Tracing
สาขาวิชา	Education	Education
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2009	1994
ผู้ริเริ่ม	Educational data mining community (Baker, Yacef, Romero, Ventura)	Albert Corbett & John Anderson
ประเภท	Application of data-mining and machine-learning methods to educational data	Two-state hidden Markov model of latent skill mastery from response sequences
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	EDM, Mining Education Data, Data Mining in Education, Learner Data Mining	BKT, Knowledge Tracing (Corbett-Anderson), Hidden Markov Knowledge Tracing, Skill Mastery Tracing
ที่เกี่ยวข้อง	4	3
สรุป	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.