Vertaile menetelmiä

	Educational Data Mining	Päätöspuu
Tieteenala	Education	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2009	1984
Kehittäjä	Educational data mining community (Baker, Yacef, Romero, Ventura)	Breiman, Friedman, Olshen & Stone
Tyyppi	Application of data-mining and machine-learning methods to educational data	Recursive partitioning (if-then rules)
Alkuperäislähde	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.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Rinnakkaisnimet	EDM, Mining Education Data, Data Mining in Education, Learner Data Mining	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Liittyvät	4	5
Tiivistelmä	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.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.