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467 kr
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Value-added methods refer to efforts to estimate the relative contributions of specific teachers, schools, or programs to student test performance. In recent years, these methods have attracted considerable attention because of their potential applicability for educational accountability, teacher pay-for-performance systems, school and teacher improvement, program evaluation, and research. Value-added methods involve complex statistical models applied to test data of varying quality. Accordingly, there are many technical challenges to ascertaining the degree to which the output of these models provides the desired estimates. Despite a substantial amount of research over the last decade and a half, overcoming these challenges has proven to be very difficult, and many questions remain unanswered--at a time when there is strong interest in implementing value-added models in a variety of settings.The National Research Council and the National Academy of Education held a workshop, summarized in this volume, to help identify areas of emerging consensus and areas of disagreement regarding appropriate uses of value-added methods, in an effort to provide research-based guidance to policy makers who are facing decisions about whether to proceed in this direction.
High School Dropout, Graduation, and Completion Rates
Better Data, Better Measures, Better Decisions
Häftad, Engelska, 2011
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High school graduation and dropout rates have long been used as indicators of educational system productivity and effectiveness and of social and economic well being. While determining these rates may seem like a straightforward task, their calculation is in fact quite complicated. How does one count a student who leaves a regular high school but later completes a GED? How does one count a student who spends most of his/her high school years at one school and then transfers to another? If the student graduates, which school should receive credit? If the student drops out, which school should take responsibility? High School Dropout, Graduation, and Completion Rates addresses these issues and to examine (1) the strengths, limitations, accuracy, and utility of the available dropout and completion measures; (2) the state of the art with respect to longitudinal data systems; and (3) ways that dropout and completion rates can be used to improve policy and practice.