Interim assessments are increasingly common in U.S. schools. We use high-quality data from a large-scale school-level cluster randomized experiment to examine the impact of two well-known commercial interim assessment programs on mathematics and reading achievement in Indiana. Results indicate that the treatment effects are positive but not consistently significant. The treatment effects are smaller in lower grades (i.e., kindergarten to second grade) and larger in upper grades (i.e., third to eighth grade). Significant treatment effects are detected in Grades 3 to 8, especially in third- and fourth-grade reading and in fifth- and sixth-grade mathematics.
BlackP.WiliamD. (1998). Inside the black box: Raising standards through classroom assessment. Phi Delta Kappan, 80, 139–144.
2.
BlancS.ChristmanJ. B.LiuR.MitchellC.TraversE.BulkleyK. E.LawrenceN. R. (2010). Learning to learn from data: Benchmarks and instructional communities. Peabody Journal of Education, 85, 205–225.
3.
BlankR.PorterA.SmithsonJ. (2001). New tools for analyzing teaching, curriculum, and standards in mathematics and science (Report from Survey of Enacted Curriculum Project, National Science Foundation REC98-03080). Washington, DC: Council of Chief State School Officers.
4.
BoruchR.WeisburdD.BerkR. (2010). Place randomized trials. In PiqueroA.WeisburdD. (Eds.), Handbook of quantitative criminology (pp. 481–502). New York, NY: Springer.
5.
BraceyG. W. (2005). No Child Left Behind: Where does the money go? (EPSL-0506-114-EPRU). Tempe: Education Policy Studies Laboratory, Arizona State University.
6.
BulkleyK. E.ChristmanJ. B.GoertzM. E.LawrenceN. R. (2010). Building with benchmarks: The role of the district in Philadelphia’s Benchmark Assessment System. Peabody Journal of Education, 85, 186–204.
7.
CarlsonD.BormanG. D.RobinsonM. (2011). A multi-state district-level cluster randomized trial of the impact of data-driven reform on reading and mathematics achievement. Educational Evaluation and Policy Analysis, 33, 378–398.
8.
CluneW. H.WhiteP. A. (2008, October). Policy effectiveness of interim assessments in providence public schools(WCER Working Paper No. 2008-10). Madison: University of Wisconsin-Madison, Wisconsin Center for Education Research.
9.
CoeR. (2002). The role and impact of performance feedback in schools. In VischerA.CoeR. (Eds.), School improvement through performance feedback (pp. 3–26). Lisse, Netherlands: Swetz & Zeitlinger.
10.
CordrayD.PionG.BrandtC.MolefeA.TobyM. (2012). The impact of the Measures of Academic Progress (MAP) program on student reading achievement (NCEE 2013-4000). Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education.
11.
DatnowA.ParkV.WohlstetterP. (2007). Achieving with data: How high-performing school systems use data to improve instruction for elementary students. San Diego: University of Southern California, Rossier School of Education, Center on Educational Governance.
12.
DavidsonK.FrohbieterG. (2011). District adoption and implementation of interim and benchmark assessments (CRESST Report 806). Los Angeles, CA: National Center for Research on Evaluation, Standards, and Student Testing.
13.
DunnK. E.MulvenonS. W. (2009). A critical review of research on formative assessment: The limited scientific evidence of the impact of formative assessment in education. Practical Assessment Research & Evaluation, 14(7), 1–11.
14.
FariaA.HeppenJ.LiY.StachelS.JonesW.SawyerK.. . .PalaciosM. (2012). The use of interim assessment data in urban schools: Links among data use practices and student achievement. Washington, DC: American Institutes for Research.
15.
GlickJ. E.WhiteM. J. (2003). The academic trajectories of immigrant youths: Analysis within and across cohorts. Demography, 40, 759–783.
16.
GoertzM. E.Nabors-OlahL.RigganM. (2010). From testing to teaching: The use of interim assessments in classroom instruction (CPRE Research Report #RR-65). Philadelphia, PA: The Consortium for Policy Research in Education.
17.
GorenP. (2012, February). Data, data, and more data—What’s an educator to do?American Journal of Education, 118, 233–237.
18.
HattieJ.TimperleyH. (2007). The power of feedback. Review of Educational Research, 77, 88–112.
19.
HendersonS.PetrosinoA.GuckenburgS.HamiltonS. (2007). Measuring how benchmark assessments affect student achievement (Issues and Answers Report, REL 2007–No. 039). Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educational Laboratory Northeast and Islands.
20.
HeritageM. (2010). Formative assessment: Making it happen in the classroom. Thousand Oaks, CA: Corwin Press.
21.
HillC. J.BloomH. S.BlackA. R.LipseyM. W. (2008). Empirical benchmarks for interpreting effect sizes in research. Child Development Perspectives, 2, 172–177.
22.
HollandP. W.RubinD. B. (1982). Test equating. New York, NY: Academic Press.
23.
Indiana Department of Education & CTB/McGraw-Hill. (2010). Indiana statewide testing for educational progress, plus: ISTEP+ 2010 technical report. Indianapolis, IN: Author.
24.
Indiana State Board of Education. (2006). A long-term assessment plan for Indiana: Driving student learning. Indianapolis, IN: Author.
25.
KingstonN.NashB. (2011). Formative assessment: A meta-analysis and a call for research. Educational Measurement: Issues and Practice, 30(4), 28–37.
26.
KlugerA.DeNisiA. (1996). The effects of feedback interventions on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychological Bulletin, 119, 254–284.
27.
KolenM. J.BrennanR. L. (1995). Test equating: Methods and practices. New York, NY: Springer.
28.
KonstantopoulosS. (2006). Trends of school effects on student achievement: Evidence from NLS:72, HSB:82, and NELS:92. Teachers College Record, 108, 2550–2581.
29.
LiY.MarionS.PerieM.GongB. (2010). An approach for evaluating the technical quality of interim assessments. Peabody Journal of Education, 85, 163–185.
30.
LuceT.ThompsonL. (2005). Do what works: How proven practices can improve America’s public schools. Dallas, TX: Ascent Education Press.
31.
MayH.RobinsonM. A. (2007). A randomized evaluation of Ohio’s Personalized Assessment Reporting System (PARS). Philadelphia: University of Pennsylvania Consortium for Policy Research in Education.
32.
Michael & Susan Dell Foundation. (2009). Performance management report. Austin, TX: Author.
33.
Nabors-OlahL.LawrenceN. R.RigganM. (2010). Learning to learn from benchmark data: How to analyze results. Peabody Journal of Education, 85, 226–245.
34.
NyquistJ. (2003). Reconceptualizing feedback as formative assessment: A meta-analysis (Unpublished master’s thesis). Vanderbilt University, Nashville, TN.
35.
PerieM.MarionS.GongB.WurtzelJ. (2007). The role of interim assessments in a comprehensive assessment system: A policy brief. Dover, NH: National Center for the Improvement of Educational Assessment.
36.
PorterA. (2002). Measuring the content of instruction: Uses in research and practice. Educational Researcher, 31, 3–14.
37.
QuintJ.SepanikS.SmithJ. (2008). Using student data to improve teaching and learning: Findings from an evaluation of the Formative Assessments of Student Thinking in Reading (FAST-R) program in Boston elementary schools. New York, NY: MDRC.
38.
SadlerD. R. (1989). Formative assessment and the design of instructional strategies. Instructional Science, 18, 119–144.
ShadishW. R.CookT. D.CampbellD. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston, MA: Houghton Mifflin.
41.
SlavinR. E.CheungA.HolmesG. C.MaddenN. A.ChamberlainA. (2011). Effects of a data-driven district reform model. Baltimore, MD: Johns Hopkins University’s Center for Research and Reform in Education.
42.
StigginsR. (2002). Assessment crisis: The absence of assessment for learning. Phi Delta Kappan, 83, 758–767.
43.
TobiasS. (1987). Learner characteristics. In GagneR. (Ed.), Instructional design: Foundations (pp. 207–231). Hillsdale, NJ: Lawrence Erlbaum.
