Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Educational Data Mining× | Learning Analytics Method× | |
|---|---|---|
| Campo | Education | Education |
| Familia≠ | Machine learning | Process / pipeline |
| Año de origen≠ | 2009 | 2011 |
| Autor original≠ | Educational data mining community (Baker, Yacef, Romero, Ventura) | George Siemens, Ryan Baker, and the learning analytics research community |
| Tipo≠ | Application of data-mining and machine-learning methods to educational data | Applied data-analytic methodology for educational data |
| Fuente 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. link ↗ | Baker, R. S. J. d., & Inventado, P. S. (2014). Educational Data Mining and Learning Analytics. In J. A. Larusson & B. White (Eds.), Learning Analytics: From Research to Practice (pp. 61–75). Springer. DOI ↗ |
| Alias | EDM, Mining Education Data, Data Mining in Education, Learner Data Mining | Learning Analytics Pipeline, Educational Learning Data Analytics, Analytics of Learner Trace Data, Learning Analytics Workflow |
| Relacionados | 4 | 4 |
| Resumen≠ | 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. | Learning analytics is the measurement, collection, analysis, and reporting of data about learners and their contexts for the purposes of understanding and optimizing learning and the environments in which it occurs. Emerging as a distinct field around 2011, and consolidated through the work of George Siemens, Ryan Baker, and the Society for Learning Analytics Research, it is methodologically a pipeline: learner trace data are gathered from digital environments, integrated, modeled to detect patterns and predict outcomes, and then fed back to learners, instructors, and institutions to inform action. |
| ScholarGateConjunto de datos ↗ |
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