Methoden vergleichen

	Educational Data Mining	K-Means-Clustering
Fachgebiet	Education	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2009	1967
Urheber	Educational data mining community (Baker, Yacef, Romero, Ventura)	MacQueen, J.
Typ	Application of data-mining and machine-learning methods to educational data	Partitional clustering (centroid-based)
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.	MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.
Aliasnamen	EDM, Mining Education Data, Data Mining in Education, Learner Data Mining	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
Verwandt	4	3
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.	K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis.