Changing the Odds: Student Achievement after Introduction of a Middle School Math Intervention

References

• Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program. Journal of the American Statistical Association, 105(490), 493–505. https://doi.org/10.1198/jasa.2009.ap08746
   Web of Science ®Google Scholar
• Abadie, A., Diamond, A., & Hainmueller, J. (2015). Comparative politics and the synthetic control method. American Journal of Political Science, 59(2), 495–510. https://doi.org/10.1111/ajps.12116
   Web of Science ®Google Scholar
• Abadie, A., & Gardeazabal, J. (2003). The economic costs of conflict: A case study of the Basque country. American Economic Review, 93(1), 113–132. https://doi.org/10.1257/000282803321455188
   Web of Science ®Google Scholar
• Ashlock, R. (2010). Error patterns in computation: Using error patterns to help each student learn. (10th ed.). Allyn and Bacon.
   Google Scholar
• Betts, J. R., Hahn, Y., & Zau, A. C. (2017). Can testing improve student learning? An evaluation of the mathematics diagnostic testing project. Journal of Urban Economics, 100, 54–64. https://doi.org/10.1016/j.jue.2017.04.003
   Web of Science ®Google Scholar
• Blanc, S., Christman, J. B., Liu, R., Mitchell, C., Travers, E., & Bulkley, K. E. (2010). Learning to learn from data: Benchmarks and instructional communities. Peabody Journal of Education, 85(2), 205–225. https://doi.org/10.1080/01619561003685379
   Google Scholar
• Boaler, J. (2015). Mathematical mindsets: Unleashing students’ potential through creative math, inspiring messages, and innovative teaching. Jossey Bass.
   Google Scholar
• Bryk, A. S., Gomez, L. M., & Grunow, A. (2011). Getting ideas into action: Building networked improvement communities in education. In Maureen Hallinan (Ed.), Frontiers in sociology of education. Springer Publishing.
   Google Scholar
• Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2008). Bootstrap-based improvements for inference with clustered errors. Review of Economics and Statistics, 90(3), 414–427. https://doi.org/10.1162/rest.90.3.414
   Web of Science ®Google Scholar
• Cavallo, E., Galiani, S., Noy, I., & Pantano, J. (2013). Catastrophic natural disasters and economic growth. The Review of Economics and Statistics, (95(5), 1549–1561. https://doi.org/10.1162/REST_a_00413
   Web of Science ®Google Scholar
• Cosner, S. (2011). Teacher learning, instructional considerations and principal communication: Lessons from a longitudinal study of collaborative data use by teachers. Educational Management Administration & Leadership, 39(5), 568–589. https://doi.org/10.1177/1741143211408453
   Web of Science ®Google Scholar
• Daly, A. J. (2012). Data, dyads, and dynamics: Exploring data use and social networks in educational improvement. Teachers College Record: The Voice of Scholarship in Education, 114(11), 38. https://doi.org/10.1177/016146811211401103
   Web of Science ®Google Scholar
• Datnow, A., & Park, V. (2014). Data driven leadership. Jossey Bass.
   Google Scholar
• Datnow, A., & Park, V. (2019). Professional collaboration with purpose: Teacher learning towards equitable and excellent schools. Routledge.
   Google Scholar
• Deming, W. E. (1986). Out of the crisis. MIT Center for Advanced Engineering Study.
   Google Scholar
• Dong, N., & Maynard, R. (2013). PowerUp!: A tool for calculating minimum detectable effect sizes and minimum required samples sizes for experimental and quasi-experimental design studies. Journal of Research on Educational Effectiveness, 6(1), 24–67. https://doi.org/10.1080/19345747.2012.673143
   Web of Science ®Google Scholar
• Feldman, J., & Tung, R. (2001). Whole school reform: How schools use the data-based inquiry and decision making process [Paper presentation]. Paper Presented at the 82nd Annual Meeting of the American Educational Research Association in Seattle, WA.
   Google Scholar
• Honig, M. I., & Venkateswaran, N. (2012). School–central office relationships in evidence use: Understanding evidence use as a systems problem. American Journal of Education, 118(2), 199–222. https://doi.org/10.1086/663282
   Web of Science ®Google Scholar
• Light, D., Honey, M., Heinze, J., Brunner, C., Wexler, D., Mandinach, E., & Fasca, C. (2005). Linking data and learning: The Grow Network study. Educational Development Center, Inc.
   Google Scholar
• Marsh, J. A. (2012). Interventions promoting educators’ use of data: Research insights and gaps. Teachers College Record: The Voice of Scholarship in Education, 114(11), 48. https://doi.org/10.1177/016146811211401106
   Web of Science ®Google Scholar
• Means, B., Padilla, C., & Gallagher, L. (2010). Use of education data at the local level: From accountability to instructional improvement. U.S. Department of Education, Office of Planning, Evaluation, and Policy Development.
   Google Scholar
• National Assessment of Educational Progress. (2020). Mathematics and reading assessments. U.S. Department of Education, Institute of Education Sciences. Results downloaded from http://https://www.nationsreportcard.gov/ndecore/xplore/NDE.
   Google Scholar
• National Center for Education Statistics (NCES). (2014a). A first look: 2013 Mathematics and Reading: National Assessment of Educational Progress at Grades 4 and 8. NCES No. 2014–415. National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education.
   Google Scholar
• National Center for Education Statistics (NCES). (2014b). National Assessment of Educational Progress (NAEP), 2013 Mathematics and Reading Assessments. U.S. Department of Education, Institute of Education Sciences. State results downloaded from http://nationsreportcard.gov/reading_math_2013/#/state-performance6/2014.
   Google Scholar
• National Center for Education Statistics (NCES) (2014c). Trial Urban District Assessments, National Assessment of Educational Progress (NAEP), 2013 Mathematics and Reading Assessments. U.S. Department of Education, Institute of Education Sciences. District results downloaded from http://nationsreportcard.gov/reading_math_tuda_2013/#/tuda-profiles6/2014.
   Google Scholar
• OECD. (2016). PISA 2015 results (volume I): Excellence and equity in education. PISA, OECD Publishing. https://doi.org/10.1787/9789264266490-en
   Google Scholar
• Provasnik, S., Malley, L., Stephens, M., Landeros, K., Perkins, R., & Tang, J. H. (2016). Highlights from TIMSS and TIMSS advanced 2015: Mathematics and science achievement of U.S. students in Grades 4 and 8 and in advanced courses at the end of high school in an international context (NCES 2017-002). U.S. Department of Education, National Center for Education Statistics. Retrieved September 2020 from http://nces.ed.gov/pubsearch.
   Google Scholar
• Roodman, D., MacKinnon, J. G., Nielsen, M., Ørregaard, & Webb, M. D. (2019). Fast and wild: Bootstrap inference in stata using boottest. The Stata Journal: Promoting Communications on Statistics and Stata, (19(1), 4–60. https://doi.org/10.1177/1536867X19830877
   Google Scholar
• Rose, H., & Betts, J. R. (2004). The effect of high school courses on earnings. Review of Economics and Statistics, 86(2), 497–513. https://doi.org/10.1162/003465304323031076
   Web of Science ®Google Scholar
• San Diego Education Association. (2017). Collective bargaining agreement between the Board of Education San Diego Unified School District and the San Diego Education Association. Retrieved May 17, 2021, from http://www.sdea.net/wp-content/uploads/SDEA-Complete-Contract-2017-2020-with-Contract-Addendum-with-2019-Reopeners.pdf.
   Google Scholar
• San Diego Unified School District. (2019). San Diego unified school district financial statements, June 30, 2019. Retrieved May 17, 2021, from https://sandiegounified.org/UserFiles/Servers/Server_27732394/File/Departments/Controller/SDUSD-6.30.2019-FS-Final.pdf.
   Google Scholar
• Webb, M. D. (2014). Reworking wild bootstrap based inference for clustered errors. Queen’s University, Department of Economics, Working Paper No. 1315. https://ideas.repec.org/p/qed/wpaper/1315.html.
   Google Scholar
• White, P., & Anderson, J. (2011). Teachers’ use of national test data to focus numeracy instruction. In J. Clark, B. Kissane, J. Mousley, T. Spencer, & S. Thornton (Eds.), Mathematics: Traditions and [new] practices (pp. 777–785). AAMT and MERGA.
   Google Scholar