Salīdzināt metodes

	Educational Data Mining	Koku lēmumu pieņemšana (Decision Tree)
Nozare	Education	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2009	1984
Autors	Educational data mining community (Baker, Yacef, Romero, Ventura)	Breiman, Friedman, Olshen & Stone
Tips	Application of data-mining and machine-learning methods to educational data	Recursive partitioning (if-then rules)
Pirmavots	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.
Citi nosaukumi	EDM, Mining Education Data, Data Mining in Education, Learner Data Mining	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Saistītās	4	5
Kopsavilkums	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.