The COVID-19 pandemic heightened tensions around standardized testing policy and prompted the United States Department of Education to allow states to request waivers from federal standardized testing requirements. Paul Bruno and Dan Goldhaber describe the waivers that states requested and received, what they suggest about how state test results might be used for different purposes and by different people, and what uses of testing seem to be most salient to policy makers. They conclude with recommendations for policy makers about how to design testing policy that can both improve educational outcomes and maintain robust political support, objectives achieved at best imperfectly by existing testing policy.
DeeT. S.JacobB.A. (2011). The impact of No Child Left Behind on student achievement. Journal of Policy Analysis and Management, 30 (3), 418–446.
2.
FazlulI.KoedelC.ParsonsE.QianC. (2021). Estimating test-score growth with a gap year in the data. (Working Paper No. 248-0121). Center for Analysis of Longitudinal Data in Education Research (CALDER).
3.
FensterwaldJ. (2021, May3). California state board likely to adopt long-awaited ‘student growth model’ to measure test scores. EdSource.
4.
FiglioD.ÖzekU. (2020). An extra year to learn English? Early grade retention and the human capital development of English learners. Journal of Public Economics, 186, 104–184.
5.
GoldhaberD.ÖzekU. (2019). How much should we rely on student test achievement as a measure of success?Educational Researcher, 48 (7), 479–483.
6.
GoodmanD.P.HambletonR.K. (2004). Student test score reports and interpretive guides: Review of current practices and suggestions for future research. Applied Measurement in Education, 17 (2), 145–220.
7.
HendersonM.B.HoustonD.PetersonP.E.WestM.R. (2019, August20). Public support grows for higher teacher pay and expanded school choice: Results from the 2019 Education Next poll. Education Next.
8.
KoretzD. (2009). Measuring up: What educational testing really tells us. Harvard University Press.
9.
KoretzD. (2017). The testing charade: Pretending to make schools better. University of Chicago Press.
10.
KuhfeldM.DominaT.HanselmanP. (2019). Validating the SEDA measures of district educational opportunities via a common assessment. AERA Open, 5 (2).
11.
MarshJ.A.PaneJ.F.HamiltonL.S. (2006). Making sense of data-driven decision making in education: Evidence from recent RAND research(OP-170-EDU). RAND Corporation.
12.
McClureP. (2005). School improvement under No Child Left Behind. Center for American Progress.
13.
McEachinA.DominaT.PennerA. (2020). Heterogeneous effects of early algebra across California middle schools. Journal of Policy Analysis and Management, 39 (3), 772–800.
14.
MulvenonS.W.StegmanC.E.RitterG. (2005). Test anxiety: A multifaceted study on the perceptions of teachers, principals, counselors, students, and parents. International Journal of Testing, 5 (1), 37–61.
15.
O’KeefeB.RotherhamA.O’Neal SchiessJ. (2021). Reshaping test and accountability in 2021 and beyond. State Education Standard, 21 (2), 7–12.
16.
PDK International. (2020). Public school priorities in a political year: 52nd Annual PDK Poll of the Public’s Attitudes Toward the Public Schools. Author.
17.
PolikoffM. (2017, March20). Proficiency vs. growth: Toward a better measure. FutureEd.
18.
RebackR.RockoffJ.SchwartzH.L. (2014). Under pressure: Job security, resource allocation, and productivity in schools under No Child Left Behind. American Economic Journal: Economic Policy, 6 (3), 207–241.
WongM.CookT.D.SteinerP.M. (2015). Adding design elements to improve time series designs: No Child Left Behind as an example of causal pattern-matching. Journal of Research on Educational Effectiveness, 8 (2), 245–279.
