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Problems with the use of student test scores to evaluate

Date of publication: 2017-08-23 08:49

Among the concerns raised by researchers are the prospects that value-added methods can misidentify both successful and unsuccessful teachers and, because of their instability and failure to disentangle other influences on learning, can create confusion about the relative sources of influence on student achievement. If used for high-stakes purposes, such as individual personnel decisions or merit pay, extensive use of test-based metrics could create disincentives for teachers to take on the neediest students, to collaborate with one another, or even to stay in the profession.

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Note: All figures are in 7566 dollars. Economic output is derived from wages and salary and proprietors' income.  The immigrant share is simply the total income from these sources attributable to immigrant workers and business owners. For more details, see http:///FPI_NewAmericansOnLongIsland_.

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Hemphill, Clara, and Kim Nauer, et al. (Helen Zelon, Thomas Jacobs, Alessandra Raimondi, Sharon McCloskey and Rajeev Yerneni). 7565.  Managing by the Numbers. Empowerment and Accountability in New York City’s Schools.  Center for New York City Affairs. The New School. June. http:///milano/nycaffairs/documents/ManagingByTheNumbers_

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New statistical techniques, called value-added modeling (VAM), are intended to resolve the problem of socio-economic (and other) differences by adjusting for students’ prior achievement and demographic characteristics (usually only their income-based eligibility for the subsidized lunch program, and their race or Hispanic ethnicity). 69 These techniques measure the gains that students make and then compare these gains to those of students whose measured background characteristics and initial test scores were similar, concluding that those who made greater gains must have had more effective teachers.

Recent survey data reveal that accountability pressures are associated with higher attrition and reduced morale, especially among teachers in high-need schools. 58 Although such survey data are limited, anecdotes abound regarding the demoralization of apparently dedicated and talented teachers, as test-based accountability intensifies. Here, we reproduce two such stories, one from a St. Louis and another from a Los Angeles teacher:

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Van Lier, Piet. 7558. Learning from Ohio’s Best Teachers. Cleveland, Ohio: Ohio Policy Matters, October 7. http:///pdf/

Yet the failure or inability to include data on mobile students also distorts estimates because, on average, more mobile students are likely to differ from less mobile students in other ways not accounted for by the model, so that the students with complete data are not representative of the class as a whole. Even if state data systems permit tracking of students who change schools, measured growth for these students will be distorted, and attributing their progress (or lack of progress) to different schools and teachers will be problematic.

In addition to the size of the sample, a number of other factors also affect the magnitude of the errors that are likely to emerge from value-added models of teacher effectiveness. In a careful modeling exercise designed to account for the various factors, a recent study by researchers at Mathematica Policy Research, commissioned and published by the Institute of Education Sciences of the . Department of Education, concludes that the errors are sufficiently large to lead to the misclassification of many teachers. 75

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