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| Educational Data Mining× | K-Means Clustering× | |
|---|---|---|
| Fagområde≠ | Education | Maskinlæring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 2009 | 1967 |
| Ophavsperson≠ | Educational data mining community (Baker, Yacef, Romero, Ventura) | MacQueen, J. |
| Type≠ | Application of data-mining and machine-learning methods to educational data | Partitional clustering (centroid-based) |
| Oprindelig kilde≠ | 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 ↗ | 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. link ↗ |
| Aliasser | EDM, Mining Education Data, Data Mining in Education, Learner Data Mining | K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering |
| Relaterede≠ | 4 | 3 |
| Resumé≠ | 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. |
| ScholarGateDatasæt ↗ |
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