A descriptive analysis of instructional coaches’ data use in science

References

• Au, W. (2007). High-stakes testing and curricular control: A qualitative metasynthesis. Educational Researcher, 36, 258–267.
   Google Scholar
• Ayres, L., Kavanaugh, K., & Knafl, K. A. (2003). Within-case and across-case approaches to qualitative data analysis. Qualitative Health Research, 13, 871–883.
   PubMed Web of Science ®Google Scholar
• Beaver, J. K., & Weinbaum, E. H. (2015). State test data and school improvement efforts. Educational Policy, 29, 478–503.
   Web of Science ®Google Scholar
• Berland, L. K., & McNeill, K. L. (2010). A learning progression for scientific argumentation: Understanding student work and designing supportive instructional contexts. Science Education, 94, 765–793. doi:10.1002/sce.20402
   Web of Science ®Google Scholar
• Berland, L. K., Schwarz, C. V., Krist, C., Kenyon, L., Lo, A. S., & Reiser, B. J. (2015). Epistemologies in practice: Making scientific practices meaningful for students. Journal of Research in Science Teaching. Advance online publication. doi:10.1002/tea.21257
   Web of Science ®Google Scholar
• Booher-Jennings, J. (2005). Below the bubble: “Educational triage” and the Texas Accountability System. American Educational Research Journal, 42, 231–268.
   Web of Science ®Google Scholar
• Breiter, A., & Light, D. (2006). Data for school improvement: Factors for designing effective information systems to support decision-making in schools. Journal of Educational Technology & Society, 9(3), 206–217.
   Web of Science ®Google Scholar
• Brown, C. J., Stroh, H. R., Fouts, J. T., & Baker, D. B. (2005). Learning to change: School coaching for systemic reform. Seattle, WA: Fouts & Associates.
   Google Scholar
• Carlson, D., Borman, G. D., & Robinson, M. (2011). A multistate 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.
   Web of Science ®Google Scholar
• Cho, V., & Wayman, J. C. (2014). Districts’ efforts for data use and computer data systems: The role of sensemaking in system use and implementation. Teachers College Record, 116(2), 1–45.
   Web of Science ®Google Scholar
• Coburn, C. E. (2001). Collective sensemaking about reading: How teachers mediate reading policy in their professional communities. Educational Evaluation and Policy Analysis, 23, 145–170.
   Web of Science ®Google Scholar
• Crawford, B. A. (2007). Learning to teach science as inquiry in the rough and tumble of practice. Journal of Research in Science Teaching, 44, 613–642. doi:10.1002/tea.20157
   Web of Science ®Google Scholar
• Crévola, C. A., & Hill, P. W. (1998). Evaluation of a whole-school approach to prevention and intervention in early literacy. Journal of Education for Students Placed at Risk, 3, 133–157.
   Google Scholar
• Crocco, M. S., & Costigan, A. T. (2007). The narrowing of curriculum and pedagogy in the age of accountability urban educators speak out. Urban Education, 42, 512–535.
   Web of Science ®Google Scholar
• Datnow, A., Park, V., & Wohlstetter, P. (2007). Achieving with data: How high-performing school systems use data to improve instruction for elementary students. Los Angeles, CA: Center on Educational Governance, University of Southern California.
   Google Scholar
• Davis, E. A., Petish, D., & Smithey, J. (2006). Challenges new science teachers face. Review of Educational Research, 76, 607–651. doi:10.3102/00346543076004607
   Web of Science ®Google Scholar
• Domina, T., Lewis, R., Agarwal, P., & Hanselman, P. (2015). Professional sense-makers instructional specialists in contemporary schooling. Educational Researcher, 44, 359–364. doi:10.3102/0013189X15601644
   Web of Science ®Google Scholar
• Duschl, R. A., & Osborne, J. (2002). Supporting and promoting argumentation discourse in science education. Studies in Science Education, 38, 39–72. doi:10.1080/03057260208560187
   Google Scholar
• Duschl, R. A., Schweingruber, H. A., & Shouse, A. W. (Eds.). (2007). Taking science to school: Learning and teaching science in Grades K-8. Washington, DC: National Academies Press.
   Google Scholar
• Elmore, R. F. (2000). Building a new structure for school leadership. Washington, DC: Albert Shanker Institute.
   Google Scholar
• Farrell, C. C., & Marsh, J. A. (2016). Metrics matter: How properties and perceptions of data shape teachers’ instructional responses. Educational Administration Quarterly, 52, 423–462.
   PubMed Web of Science ®Google Scholar
• Firestone, W. A., & Martínez, C. M. (2007). Districts, teacher leaders, and distributed leadership: Changing instructional practice. Leadership and Policy in Schools, 6, 3–35.
   Google Scholar
• Fullan, M., & Knight, J. (2011). Coaches as system leaders. Educational leadership, 69(2), 50–53.
   Web of Science ®Google Scholar
• Gallagher, L., Means, B., & Padilla, C. (2008). Teachers’ use of student data systems to improve instruction: 2005 to 2007. Washington, DC: US Department of Education.
   Google Scholar
• Hamilton, L. S., Berends, M., & Stecher, B. M. (2005). Teachers’ responses to standards-based accountability. Santa Monica, CA: RAND.
   Google Scholar
• Hofstein, A., & Lunetta, V. N. (2004). The laboratory in science education: Foundations for the twenty-first century. Science Education, 88, 28–54. doi:10.1002/sce.10106
   Web of Science ®Google Scholar
• Horn, I. S., Kane, B. D., & Wilson, J. (2015). Making sense of student performance data: Data use logics and mathematics teachers’ learning opportunities. American Educational Research Journal, 52, 208–242. doi:10.3102/0002831215573773
   Web of Science ®Google Scholar
• Huguet, A., Marsh, J. A., & Farrell, C. C. (2014). Building teachers’ data-use capacity: Insights from strong and developing Coaches. Education Policy Analysis Archives, 22(52). doi:10.14507/epaa.v22n52.2014
   Google Scholar
• Kerr, K. A., Marsh, J. A., Ikemoto, G. S., Darilek, H., & Barney, H. (2006). Strategies to promote data use for instructional improvement: Actions, outcomes, and lessons from three urban districts. American Journal of Education, 112, 496–520.
   Web of Science ®Google Scholar
• Knight, J. (2007). Instructional coaching: A partnership approach to improving instruction. Newbury Park, CA: Corwin Press.
   Google Scholar
• Kolodner, J. L., Camp, P. J., Crismond, D., Fasse, B., Gray, J., Holbrook, J., … & Ryan, M. (2003). Problem-based learning meets case-based reasoning in the middle-school science classroom: Putting learning by design (tm) into practice. The Journal of the Learning Sciences, 12, 495–547.
   Web of Science ®Google Scholar
• Krajcik, J., McNeill, K. L., & Reiser, B. J. (2008). Learning-goals-driven design model: Developing curriculum materials that align with national standards and incorporate project-based pedagogy. Science Education, 92, 1–32.
   Web of Science ®Google Scholar
• Lachat, M. A., & Smith, S. (2005). Practices that support data use in urban high schools. Journal of Education for Students Placed at Risk, 10, 333–349.
   Google Scholar
• Loughran, J., Mulhall, P., & Berry, A. (2004). In search of pedagogical content knowledge in science: Developing ways of articulating and documenting professional practice. Journal of Research in Science Teaching, 41, 370–391.
   Web of Science ®Google Scholar
• Mandinach, E. B., Honey, M., Light, D., & Brunner, C. (2008). A conceptual framework for data-driven decision making. In E. B. Mandinach & M. Honey (Eds.), Data-driven school improvement: Linking data and learning (pp. 13–31). New York, NY: Teachers College Press.
   Google Scholar
• Marsh, J. A., Bertrand, M., & Huguet, A. (2015). Using data to alter instructional practice: The mediating role of coaches and professional learning communities. Teachers College Record, 117(4), 1–40.
   Web of Science ®Google Scholar
• Marsh, J. A., McCombs, J. S., & Martorell, F. (2010). How instructional coaches support data-driven decision making policy implementation and effects in Florida middle schools. Educational Policy, 24, 872–907.
   Web of Science ®Google Scholar
• Marsh, J. A., Pane, J. F., & Hamilton, S. (2006). Making sense of data-driven decision making in education: Evidence from recent RAND research. Santa Monica, CA: RAND Corporation.
   Google Scholar
• Matsumura, L. C., Garnier, H. E., & Resnick, L. B. (2010). Implementing literacy coaching: The role of school social resources. Educational Evaluation and Policy Analysis, 32, 249-–272.
   Web of Science ®Google Scholar
• McNaughton, S., Lai, M. K., & Hsiao, S. (2012). Testing the effectiveness of an intervention model based on data use: A replication series across clusters of schools. School Effectiveness and School Improvement, 23, 203–228.
   Web of Science ®Google Scholar
• McNeill, K. L. & Krajcik, J. (2008). Inquiry and scientific explanations: Helping students use evidence and reasoning. In J. Luft, R. L. Bell, & J. Gess-Newsome (Eds.), Science as inquiry in the secondary setting (pp. 121–134). Arlington, VA: National Science Teachers Association Press.
   Google Scholar
• Means, B., Padilla, C., DeBarger, A., & Bakia, M. (2009). Implementing data-informed decision making in schools: Teacher access, supports and use. Washington, DC: US Department of Education.
   Google Scholar
• Means, B., Padilla, C., & Gallagher, L. (2010). Use of education data at the local level: From accountability to instructional improvement. Washington, DC: US Department of Education.
   Google Scholar
• National Research Council. (2000). Inquiry and the national science education standards. Washington, DC: National Academies Press.
   Google Scholar
• National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. Washington, DC: National Academies Press.
   Google Scholar
• Neufeld, B., & Roper, D. (2003). Coaching: A strategy for developing institutional capacity, promises and practicalities. Washington, DC: The Aspen Institute Program on Education, & Providence, RI: Annenberg Institute for School Reform.
   Google Scholar
• O’Day, J. A. (2002). Complexity, accountability, and school improvement. Harvard Educational Review, 72, 293–329.
   Web of Science ®Google Scholar
• Osborne, J. (2014). Teaching scientific practices: Meeting the challenge of change. Journal of Science Teacher Education, 25, 177–196.
   Google Scholar
• Palmer, D., & Lynch, A. W. (2008). A bilingual education for a monolingual test? The pressure to prepare for TAKS and its influence on choices for language of instruction in Texas elementary bilingual classrooms. Language Policy, 7, 217–235.
   Google Scholar
• Palmer, D., & Snodgrass Rangel, V. (2011). High stakes accountability and policy implementation: Teacher decision making in bilingual classrooms in Texas. Educational Policy, 25, 614–647.
   Web of Science ®Google Scholar
• Quint, J. C., Sepanik, S., & Smith, J. K. (2008). Using student data to improve teaching and learning: Findings from an evaluation of the Formative Assessments of Students Thinking in Reading (FAST-R) Program in Boston elementary schools. Boston, MA: MDRC.
   Google Scholar
• Snodgrass Rangel, V., Monroy, C., & Bell, E. R. (2016, August). Science teachers’ data use practices: A descriptive analysis. Education policy analysis archives, 24(86). doi:10.14507/epaa.24.2348
   Google Scholar
• Songer, N. B., & Gotwals, A. W. (2012). Guiding explanation construction by children at the entry points of learning progressions. Journal of Research in Science Teaching, 49, 141–165. doi:10.1002/tea.20454
   Web of Science ®Google Scholar
• Songer, N. B., & Ruiz-Primo, M. A. (2012). Assessment and science education: Our essential new priority? Journal of Research in Science Teaching, 49, 683–690. doi:10.1002/tea.21033
   Web of Science ®Google Scholar
• Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4–14.
   Google Scholar
• Shulman, L. (1987). Knowledge and teaching: Foundations of the new reform. Harvard Educational Review, 57, 1–22.
   Web of Science ®Google Scholar
• Spillane, J. P., Halverson, R., & Diamond, J. B. (2001). Investigating school leadership practice: A distributed perspective. Educational Researcher, 30(3), 23–28.
   Google Scholar
• Stecher, B., Hamilton, L. S., & Gonzalez, G. (2003). Working smarter to leave no child behind. Santa Monica: CA: Rand Corporation.
   Google Scholar
• Strauss, A., & Corbin, J. M. (1990). Basics of qualitative research: Grounded theory procedures and techniques. Thousand Oaks, CA: Sage.
   Google Scholar
• Supovitz, J. (2012). Getting at student understanding – The key to teachers’ use of test data. Teachers College Record, 114(11), 1–29.
   Web of Science ®Google Scholar
• Supovitz, J. A., & Klein, V. (2003). Mapping a course for improved student learning: How innovative schools systematically use student performance data to guide improvement (CPRE Research Report). Philadelphia, PA: Consortium for Policy Research in Education, University of Pennsylvania.
   Google Scholar
• Swinnerton, J. (2007). Brokers and boundary crossers in an urban school district: Understanding central-office coaches as instructional leaders. Journal of School Leadership, 17, 195–221.
   Google Scholar
• Texas Education Agency. (2014). 2014–15 Texas academic performance reports. Retrieved from https://rptsvr1.tea.texas.gov/perfreport/tapr/2015/index.html
   Google Scholar
• Van Dijk, E. M. (2014). Understanding the heterogeneous nature of science: A comprehensive notion of PCK for scientific literacy. Science Education, 98, 397–411.
   Web of Science ®Google Scholar
• Van Geel, M., Keuning, T., Visscher, A. J., & Fox, J. P. (2016). Assessing the effects of a school-wide data-based decision-making intervention on student achievement growth in primary schools. American Educational Research Journal, 53, 360–394. doi:10.3102/0002831216637346
   Web of Science ®Google Scholar
• Wayman, J. C., Cho, V., Jimerson, J. B., & Snodgrass Rangel, V. W. (2015). A look into the workings of data use in a mid-sized district. In I. E. Sutherland, K. L. Sanzo, & J. P. Scribner (Eds.), Leading small and mid-sized urban school districts (pp. 241–276). Bingley, UK: Emerald.
   Google Scholar
• Wayman, J. C., Cho, V., Jimerson, J. B., & Spikes, D. D. (2012). District-wide effects on data use in the classroom. Education Policy Analysis Archives, 20(25). doi:10.14507/epaa.v20n25.2012
   Google Scholar
• Wayman, J. C., Cho, V., & Johnston, M. T. (2007). The data-informed district: A district-wide evaluation of data use in the Natrona County School District. Austin, TX: The University of Texas at Austin.
   Google Scholar
• Wayman, J. C., Shaw, S. M., & Cho, V. (2011). Second-year results from an efficacy study of the Acuity data system. Retrieved from http://www.waymandatause.com/wp-content/uploads/2013/11/Wayman_Shaw_and_Cho_Year_2_Acuity_report.pdf
   Google Scholar
• Wayman, J. C., Snodgrass Rangel, V. W., Jimerson, J. B., & Cho, V. (2010). Improving data use in NISD: Becoming a data-informed district. Austin, TX: The University of Texas. Retrieved from http://www.waymandatause.com/wp-content/uploads/2013/11/Wayman-Rangel-Jimerson-Cho-theDID1.pdf
   Google Scholar
• Wayman, J. C., & Stringfield, S. (2006). Technology-supported involvement of entire faculties in examination of student data for instructional improvement. American Journal of Education, 112, 549–571.
   Web of Science ®Google Scholar
• Weick, K. E. (1995). Sensemaking in organizations. Thousand Oaks, CA: Sage.
   Google Scholar
• West, L., & Staub, F. C. (2003). Content-focused coaching: Transforming mathematics lessons. Portsmouth, NH: Heinemann.
   Google Scholar
• Wiggins, G. (1990). The case for authentic assessment. Washington, DC: ERIC Clearinghouse on Tests, Measurement, and Evaluation.
   Google Scholar
• Wills, J. S. (2007). Putting the squeeze on social studies: Managing teaching dilemmas in subject areas excluded from state testing. The Teachers College Record, 109(8), 1980–2046.
   Web of Science ®Google Scholar
• York-Barr, J., & Duke, K. (2004). What do we know about teacher leadership? Findings from two decades of scholarship. Review of Educational Research, 74, 255–316.
   Web of Science ®Google Scholar
• Young, V. M. (2006). Teachers’ use of data: Loose coupling, agenda setting, and team norms. American Journal of Education, 112, 521–548.
   Web of Science ®Google Scholar
• Zuiker, S., & Whitaker, J. R. (2014). Refining inquiry with multi-form assessment: Formative and summative assessment functions for flexible inquiry. International Journal of Science Education, 36, 1037–1059.
   Web of Science ®Google Scholar