Compara mètodes
Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| Learning Analytics Method× | Educational Growth Curve Modeling× | |
|---|---|---|
| Camp | Education | Education |
| Família≠ | Process / pipeline | Regression model |
| Any d'origen≠ | 2011 | 1987 |
| Autor original≠ | George Siemens, Ryan Baker, and the learning analytics research community | Anthony Bryk & Stephen Raudenbush; Judith Singer & John Willett |
| Tipus≠ | Applied data-analytic methodology for educational data | Longitudinal multilevel model of individual change |
| Font seminal≠ | 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 ↗ | Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press. ISBN: 9780195152968 |
| Àlies | Learning Analytics Pipeline, Educational Learning Data Analytics, Analytics of Learner Trace Data, Learning Analytics Workflow | Latent Growth Curve Modeling in Education, Multilevel Growth Models for Achievement, Individual Growth Trajectory Analysis, Learning Trajectory Modeling |
| Relacionats | 4 | 4 |
| Resum≠ | 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. | Educational growth curve modeling is a longitudinal multilevel technique for describing and explaining how individual students change over time on an outcome such as reading or mathematics achievement. Building on the hierarchical linear models framework formalized by Bryk and Raudenbush (1987) and the applied longitudinal treatment of Singer and Willett (2003), it fits each student a personal trajectory — an intercept and one or more slopes — and then models how those personal growth parameters vary across students and relate to learner characteristics, classrooms, and schools. |
| ScholarGateConjunt de dades ↗ |
|
|