방법 비교

	Value-Added Teacher Evaluation	Educational Production Function
분야	Education	Education
계열	Regression model	Regression model
기원 연도	2004	1979
창시자	William Sanders (TVAAS); methodological critique by McCaffrey, Lockwood, Koretz et al.	Economics of education (Coleman; Hanushek; Todd & Wolpin)
유형	Statistical estimation of individual teachers' contributions to student achievement growth	Regression relating educational inputs to achievement outputs
원전	McCaffrey, D. F., Lockwood, J. R., Koretz, D., Louis, T. A., & Hamilton, L. (2004). Models for value-added modeling of teacher effects. Journal of Educational and Behavioral Statistics, 29(1), 67–101.	Hanushek, E. A. (1979). Conceptual and empirical issues in the estimation of educational production functions. Journal of Human Resources, 14(3), 351–388.
별칭	Teacher Value-Added Models, VAM for Teachers, Teacher Effect Estimation, Value-Added Teacher Accountability	Education Production Function, Schooling Production Function, Input-Output Model of Education, Achievement Production Function
관련	4	3
요약	Value-added teacher evaluation uses longitudinal student test scores to estimate how much individual teachers contribute to their students' achievement growth, net of what students brought into the classroom. Statistically it applies value-added and mixed-model machinery — controlling for prior achievement and student characteristics, then treating each teacher's residual contribution as an effect to be estimated. Pioneered in Tennessee's TVAAS and scrutinized in a large methodological and policy literature, it became central, and controversial, in teacher accountability.	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.