Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Opportunity-to-Learn Index× | Educational Hierarchical Linear Modeling× | |
|---|---|---|
| Область | Education | Education |
| Семейство≠ | Process / pipeline | Regression model |
| Год появления≠ | 1995 | 2002 |
| Автор метода≠ | Carroll (concept); Husén/IEA (measurement); McDonnell; Schmidt (TIMSS) | Stephen Raudenbush & Anthony Bryk |
| Тип≠ | Quantitative index of students' exposure to instructional content and resources | Multilevel regression for hierarchically nested educational data |
| Основополагающий источник≠ | 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 ↗ | Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 9780761919049 |
| Другие названия | OTL Index, Content Coverage Index, Curriculum Exposure Measure, Opportunity-to-Learn Measurement | Multilevel Models in Education, Students-in-Schools HLM, School Effects Multilevel Model, Random-Effects Models for Educational Data |
| Связанные | 4 | 4 |
| Сводка≠ | An opportunity-to-learn (OTL) index quantifies how much exposure students have had to the content and instructional resources they need to succeed on an assessment. Rooted in Carroll's model of school learning and developed through the IEA international studies, OTL measurement asks whether students were actually taught the material before being tested on it. Constructed from teacher reports, curriculum analysis, or instructional logs, OTL indices are used both as a fairness criterion for interpreting test scores and as a policy instrument for monitoring equitable access to the intended curriculum. | Educational hierarchical linear modeling (HLM) is a multilevel regression framework for data in which students are nested within classrooms and classrooms within schools. Formalized for education by Raudenbush and Bryk, it lets the intercept and slopes of a student-level regression vary across schools, simultaneously estimating student-level relationships, school-level relationships, and the cross-level interactions between them — while producing correct standard errors that single-level regression on clustered data cannot. |
| ScholarGateНабор данных ↗ |
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