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| Educational Data Mining× | Entscheidungsbaum× | |
|---|---|---|
| Fachgebiet≠ | Education | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2009 | 1984 |
| Urheber≠ | Educational data mining community (Baker, Yacef, Romero, Ventura) | Breiman, Friedman, Olshen & Stone |
| Typ≠ | Application of data-mining and machine-learning methods to educational data | Recursive partitioning (if-then rules) |
| Wegweisende Quelle≠ | 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. link ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ |
| Aliasnamen≠ | EDM, Mining Education Data, Data Mining in Education, Learner Data Mining | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| Verwandt≠ | 4 | 5 |
| Zusammenfassung≠ | 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. |
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