השוואת שיטות

	Opportunity-to-Learn Index	Educational Production Function
תחום	Education	Education
משפחה	Process / pipeline	Regression model
שנת המקור	1995	1979
הוגה השיטה	Carroll (concept); Husén/IEA (measurement); McDonnell; Schmidt (TIMSS)	Economics of education (Coleman; Hanushek; Todd & Wolpin)
סוג	Quantitative index of students' exposure to instructional content and resources	Regression relating educational inputs to achievement outputs
מקור מכונן	McDonnell, L. M. (1995). Opportunity to learn as a research concept and a policy instrument. Educational Evaluation and Policy Analysis, 17(3), 305–322.	Hanushek, E. A. (1979). Conceptual and empirical issues in the estimation of educational production functions. Journal of Human Resources, 14(3), 351–388.
כינויים	OTL Index, Content Coverage Index, Curriculum Exposure Measure, Opportunity-to-Learn Measurement	Education Production Function, Schooling Production Function, Input-Output Model of Education, Achievement Production Function
קשורות	4	3
תקציר	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.	The educational production function is the economist's framework for relating the inputs of schooling — class size, teacher quality, expenditure, family background — to an output, usually measured achievement. Borrowing the production-function metaphor from the economics of the firm, it estimates by how much achievement changes when an input changes. It is the analytic backbone of decades of debate over what resources matter for learning, and the methodological challenges of estimating it honestly — endogeneity, omitted variables, and the cumulative history of inputs — define much of the field.