Σύγκριση μεθόδων

	Opportunity to Learn Analysis	Educational Hierarchical Linear Modeling
Πεδίο	Education	Education
Οικογένεια	Process / pipeline	Regression model
Έτος προέλευσης	1963	2002
Δημιουργός	John B. Carroll (1963); Lorraine McDonnell (1995); IEA surveys	Stephen Raudenbush & Anthony Bryk
Τύπος	Measurement and analysis of students' exposure to instructional content	Multilevel regression for hierarchically nested educational data
Θεμελιώδης πηγή	McDonnell, L. M. (1995). Opportunity to learn as a research concept and a policy instrument. Educational Evaluation and Policy Analysis, 17(3), 305–322.	Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 9780761919049
Εναλλακτικές ονομασίες	OTL Analysis, Opportunity-to-Learn Indicators, Content Coverage Analysis, Curriculum Coverage Measurement	Multilevel Models in Education, Students-in-Schools HLM, School Effects Multilevel Model, Random-Effects Models for Educational Data
Συναφείς	4	4
Σύνοψη	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.	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.