Compara mètodes

	Educational Data Mining	Arbre de decisió
Camp	Education	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	2009	1984
Autor original	Educational data mining community (Baker, Yacef, Romero, Ventura)	Breiman, Friedman, Olshen & Stone
Tipus	Application of data-mining and machine-learning methods to educational data	Recursive partitioning (if-then rules)
Font seminal	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.
Àlies	EDM, Mining Education Data, Data Mining in Education, Learner Data Mining	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Relacionats	4	5
Resum	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.