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| Opportunity to Learn Analysis× | Learning Analytics Method× | |
|---|---|---|
| Bidang | Education | Education |
| Keluarga | Process / pipeline | Process / pipeline |
| Tahun asal≠ | 1963 | 2011 |
| Pengasas≠ | John B. Carroll (1963); Lorraine McDonnell (1995); IEA surveys | George Siemens, Ryan Baker, and the learning analytics research community |
| Jenis≠ | Measurement and analysis of students' exposure to instructional content | Applied data-analytic methodology for educational data |
| Sumber perintis≠ | McDonnell, L. M. (1995). Opportunity to learn as a research concept and a policy instrument. Educational Evaluation and Policy Analysis, 17(3), 305–322. DOI ↗ | 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 | OTL Analysis, Opportunity-to-Learn Indicators, Content Coverage Analysis, Curriculum Coverage Measurement | Learning Analytics Pipeline, Educational Learning Data Analytics, Analytics of Learner Trace Data, Learning Analytics Workflow |
| Berkaitan | 4 | 4 |
| Ringkasan≠ | Opportunity to learn (OTL) analysis measures the degree to which students are actually taught the content on which they are assessed, and relates that exposure to their achievement. Rooted in Carroll's 1963 model of school learning and developed as both a research concept and a policy instrument by McDonnell (1995) and the international IEA assessments, it treats content coverage, instructional time, and the alignment between the enacted curriculum and the tested curriculum as measurable conditions of learning rather than properties of the learner. | 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. |
| ScholarGateSet data ↗ |
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