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Value-added modeling

Value-added modeling (also known as value-added analysis and value-added assessment) is a method of teacher evaluation that measures the teacher's contribution in a given year by comparing the current test scores of their students to the scores of those same students in previous school years, as well as to the scores of other students in the same grade. In this manner, value-added modeling seeks to isolate the contribution, or value added, that each teacher provides in a given year, which can be compared to the performance measures of other teachers. VAMs are considered to be fairer than simply comparing student achievement scores or gain scores without considering potentially confounding context variables like past performance or income. It is also possible to use this approach to estimate the value added by the school principal or the school as a whole.

Critics say that the use of tests to evaluate individual teachers has not been scientifically validated, and much of the results are due to chance or conditions beyond the teacher's control, such as outside tutoring. Research shows, however, that differences in teacher effectiveness as measured by value-added of teachers are associated with very large economic effects on students.

Researchers use statistical processes on a student's past test scores to predict the student's future test scores, on the assumption that students usually score approximately as well each year as they have in past years. The student's actual score is then compared to the predicted score. The difference between the predicted and actual scores, if any, is assumed to be due to the teacher and the school, rather than to the student's natural ability or socioeconomic circumstances.

In this way, value-added modeling attempts to isolate the teacher's contributions from factors outside the teacher's control that are known to strongly affect student test performance, including the student's general intelligence, poverty, and parental involvement.

By aggregating all of these individual results, statisticians can determine how much a particular teacher improves student achievement, compared to how much the typical teacher would have improved student achievement.

Statisticians use hierarchical linear modeling to predict the score for a given student in a given classroom in a given school. This prediction is based on aggregated results of all students. Each student's predicted score may take into account student level (e.g., past performance, socioeconomic status, race/ethnicity), teacher level (e.g., certification, years of experience, highest degree earned, teaching practices, instructional materials, curriculum) and school level (e.g., size, type, setting) variables into consideration. Which variables are included depends on the model.



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