Tools of the trade: a look at educators’ use of assessment systems

References

• Abrams, L. M., McMillan, J. H., & Wetzel, A. P. (2015). Implementing benchmark testing for formative purposes: Teacher voices about what works. Educational Assessment, Evaluation and Accountability, 27(4), 347–375. https://doi.org/10.1007/s11092-015-9214-9
   Web of Science ®Google Scholar
• Bertrand, M., & Marsh, J. A. (2015). Teachers’ sensemaking of data and implications for equity. American Educational Research Journal, 52(5), 861–893. https://doi.org/10.3102/0002831215599251
   Web of Science ®Google Scholar
• Bickel, W. E., & Cooley, W. W. (1985). Decision-oriented educational research in school districts: The role of dissemination processes. Studies in Educational Evaluation, 11(2), 183–203. https://doi.org/10.1016/0191-491X(85)90022-7
   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
• Breiter, A., & Light, D. (2006). Data for school improvement: Factors for designing effective information systems to support decision-making in schools. Educational Technology & Society, 9(3), 206–217.
   Web of Science ®Google Scholar
• Brimijoin, K., Marquissee, E., & Tomlinson, C. A. (2003). Using data to differentiate instruction. Educational Leadership, 60(5), 70–73.
   Web of Science ®Google Scholar
• Brunner, C., Fasca, C., Heinze, J., Honey, M., Light, D., Mardinach, E., & Wexler, D. (2005). Linking data and learning: The Grow Network study. Journal of Education for Students Placed at Risk (JESPAR), 10(3), 241–267. https://doi.org/10.1207/s15327671espr1003_2
   Google Scholar
• Burch, P. (2009). Hidden markets: The new education privatization. Routledge.
   Google Scholar
• Burch, P. E. (2006). The new educational privatization: Educational contracting and high stakes accountability. Teachers College Record, 108(12), 2582–2610. doi: 10.1111/j.1467-9620.2006.00797.x
   Web of Science ®Google Scholar
• Burton-Jones, A., & Straub, D. W., Jr. (2006). Reconceptualizing system usage: An approach and empirical test. Information Systems Research, 17(3), 228–246. doi: 10.1287/isre.1060.0096
   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
• Christman, J. B., Neild, R. C., Bulkley, K., Blanc, S., Liu, R., Mitchell, C., & Travers, E. (2009). Making the most of interim assessment data: Lessons from Philadelphia. Research for Action.
   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(2), 145–170. https://doi.org/10.3102/01623737023002145
   Web of Science ®Google Scholar
• Coburn, C. E., Toure, J., & Yamashita, M. (2009). Evidence, interpretation, and persuasion: Instructional decision making at the district central office. Teachers College Record, 111(4), 1115–1161.
   Web of Science ®Google Scholar
• Coburn, C. E., & Turner, E. O. (2011). Research on data use: A framework and analysis. Measurement: Interdisciplinary Research and Perspectives, 9(4), 173–206. https://doi.org/10.1080/15366367.2011.626729
   Google Scholar
• Cosner, S. (2011). Supporting the initiation and early development of evidence-based grade-level collaboration in urban elementary schools: Key roles and strategies of principals and literacy coordinators. Urban Education, 46(4), 786–827. https://doi.org/10.1177/0042085911399932
   Web of Science ®Google Scholar
• Datnow, A., & Hubbard, L. (2015). Teachers’ use of assessment data to inform instruction: Lessons from the past and prospects for the future. Teachers College Record, 117(4), 1–26.
   Web of Science ®Google Scholar
• Datnow, A., & Park, V. (2014). Data-driven leadership. Jossey-Bass.
   Google Scholar
• Davidson, K. L., & Frohbieter, G. (2011). District adoption and implementation of interim and benchmark assessments (CRESST Report No. 806). University of California, National Center for Research on Evaluation, Standards, and Student Testing.
   Google Scholar
• Drake, T. A. (2015, April 16–20). Exploring the factors and conditions associated with principals’ use of district data systems [Paper presentation]. Annual Meeting of the American Educational Research Association, Chicago, IL, United States.
   Google Scholar
• Farley-Ripple, E. N., & Buttram, J. L. (2014a). Developing collaborative data use through professional learning communities: Early lessons from Delaware. Studies in Educational Evaluation, 42, 41–53. https://doi.org/10.1016/j.stueduc.2013.09.006
   Web of Science ®Google Scholar
• Farley-Ripple, E. N., & Buttram, J. L. (2014b). Schools’ use of interim data: Practices in classrooms, teams, and schools. In A. J. Bowers, A. R. Shoho, & B. G. Barnett (Eds.), International research on school leadership: Vol. 5. Using data in schools to inform leadership and decision making (pp. 39–66). Information Age Publishing.
   Google Scholar
• Gotch, C. M., & Roberts, M. R. (2018). A review of recent research on individual-level score reports. Educational Measurement: Issues and Practice, 37(3), 46–54. https://doi.org/10.1111/emip.12198
   Web of Science ®Google Scholar
• Hambleton, R. K., & Zenisky, A. L. (2013). Reporting test scores in more meaningful ways: A research-based approach to score report design. In K. F. Geisinger, B. A. Bracken, J. F. Carlson., J.-I. C. Hansen, N. R. Kuncel, S. P. Reise, & M. C. Rodriguez (Eds.), APA handbook of testing and assessment in psychology: Vol. 3. Testing and assessment in school psychology and education (pp. 479–494). American Psychological Association. https://doi.org/10.1037/14049-023
   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(2), 208–242. https://doi.org/10.3102/0002831215573773
   Web of Science ®Google Scholar
• Ikemoto, G. S., & Marsh, J. A. (2007). Chapter 5 Cutting through the “data-driven” mantra: Different conceptions of data-driven decision making. Yearbook of the National Society for the Study of Education, 106(1), 105–131. https://doi.org/10.1111/j.1744-7984.2007.00099.x
   Google Scholar
• Jimerson, J. B. (2014). Thinking about data: Exploring the development of mental models for “data use” among teachers and school leaders. Studies in Educational Evaluation, 42, 5–14. https://doi.org/10.1016/j.stueduc.2013.10.010
   Web of Science ®Google Scholar
• Knapp, M. S., Copland, M. A., & Swinnerton, J. A. (2007). Chapter 4 Understanding the promise and dynamics of data-informed leadership. Yearbook of the National Society for the Study of Education, 106(1), 74–104. doi: 10.1111/j.1744-7984.2007.00098.x
   Google Scholar
• Lai, M. K., & Hsiao, S. (2014). Developing data collection and management systems for decision-making: What professional development is required? Studies in Educational Evaluation, 42, 63–70. https://doi.org/10.1016/j.stueduc.2013.12.006
   Web of Science ®Google Scholar
• Little, J. W., & Curry, M. W. (2009). Structuring talk about teaching and learning: The use of evidence in protocol-based conversation. In L. M. Earl & H. Timperley (Eds.), Professional learning conversations: Challenges in using evidence for improvement (pp. 29–42). Springer.
   Google Scholar
• Mandinach, E. B., & Gummer, E. S. (2013). A systemic view of implementing data literacy in educator preparation. Educational Researcher, 42(1), 30–37. https://doi.org/10.3102/0013189X12459803
   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). Teachers College Press.
   Google Scholar
• Marsh, J. (2012). Interventions promoting educators’ use of data: Research insights and gaps. Teachers College Record, 114(11), 1–48.
   PubMed Web of Science ®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., & Farrell, C. C. (2015). How leaders can support teachers with data-driven decision making: A framework for understanding capacity building. Educational Management Administration & Leadership, 43(2), 269–289. https://doi.org/10.1177/1741143214537229
   Web of Science ®Google Scholar
• Marsh, J. A., Pane, J. F., & Hamilton, L. S. (2006). Making sense of data-driven decision making in education: Evidence from recent RAND research (Occasional Paper No. 170). RAND Corporation.
   Google Scholar
• Means, B., Padilla, C., DeBarger, A., & Bakia, M. (2009). Implementing data-informed decision making in schools – Teacher access, supports and use. U.S. Department of Education, Office of Planning, Evaluation and Policy Development.
   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.
   Google Scholar
• Nabors-Oláh, L., Lawrence, N., & Riggan, M. (2010). Learning to learn from benchmark assessment data: How teachers analyze results. Peabody Journal of Education, 85(2), 226–245. https://doi.org/10.1080/01619561003688688
   Google Scholar
• Norman, D. A. (1988). The design of everyday things. Basic Books.
   Google Scholar
• Perie, M., Marion, S., & Gong, B. (2009). Moving toward a comprehensive assessment system: A framework for considering interim assessments. Educational Measurement: Issues and Practice, 28(3), 5–13. https://doi.org/10.1111/j.1745-3992.2009.00149.x
   Google Scholar
• Pierce, R., & Chick, H. (2010, July 11–16). Interpreting literacy and numeracy testing reports: What do teachers need to know. In C. Reading (Ed.), Data and context in statistics education: Towards an evidence-based society. Proceedings of the 8th International Conference on Teaching Statistics (ICOTS-8). International Association for Statistical Education; International Statistical Institute.
   Google Scholar
• Pierce, R., Chick, H., & Wander, R. (2014). Improving teachers’ professional statistical literacy. In H. MacGillivray, M. A. Martin, & B. Phillips (Eds.), Topics from Australian conferences on teaching statistics (pp. 295–309). Springer.
   Google Scholar
• Piety, P. (2011). Educational data use: A sociotechnical process. Measurement: Interdisciplinary Research and Perspectives, 9(4), 217–221. https://doi.org/10.1080/15366367.2011.632335
   Google Scholar
• Ruggles, R. L., III. (2009). Knowledge management tools. Routledge.
   Google Scholar
• Ryan, J. M. (2006). Practices, issues, and trends in student test score reporting. In S. M. Downing & T. M. Haladyna (Eds.), Handbook of test development (pp. 677–710). Routledge.
   Google Scholar
• Schildkamp, K., Karbautzki, L., & Vanhoof, J. (2014). Exploring data use practices around Europe: Identifying enablers and barriers. Studies in Educational Evaluation, 42, 15–24. https://doi.org/10.1016/j.stueduc.2013.10.007
   Web of Science ®Google Scholar
• Schildkamp, K., & Poortman, C. (2015). Factors influencing the functioning of data teams. Teachers College Record, 117(4).
   Web of Science ®Google Scholar
• Schildkamp, K., Poortman, C. L., & Handelzalts, A. (2016). Data teams for school improvement. School Effectiveness And School Improvement, 27(2), 228–254. https://doi.org/10.1080/09243453.2015.1056192
   Web of Science ®Google Scholar
• Shepard, L. A., Davidson, K. L., & Bowman, R. (2011). How middle-school mathematics teachers use interim and benchmark assessment data (CRESST Report No. 807). National Center for Research on Evaluation, Standards, and Student Testing (CRESST).
   Google Scholar
• Sherer, J. Z., & Spillane, J. (2011). Constancy and change in work practice in schools: The role of organizational routines. Teachers College Record, 113(3), 661–657.
   Web of Science ®Google Scholar
• Spillane, J. P. (2012). Data in practice: Conceptualizing the data-based decision-making phenomena. American Journal of Education, 118(2), 113–141. https://doi.org/10.1086/663283
   Web of Science ®Google Scholar
• Spillane, J. P., Reiser, B. J., & Reimer, T. (2002). Policy implementation and cognition: Reframing and refocusing implementation research. Review of Educational Research, 72(3), 387–431. https://doi.org/10.3102/00346543072003387
   Web of Science ®Google Scholar
• Supovitz, J. A., & Morrison, K. (2011, April 8–12). Does collaboration facilitate data use in schools? [Paper presentation]. Annual Meeting of the American Educational Research Association, New Orleans, LA, United States.
   Google Scholar
• Timperley, H. (2009). Evidence-informed conversations making a difference to student achievement. In L. M. Earl & H. Timperley (Eds.), Professional learning conversations: Challenges in using evidence for improvement (pp. 69–79). Springer.
   Google Scholar
• Tyler, J. H. (2013). If you build it will they come? Teachers’ online use of student performance data. Education Finance and Policy, 8(2), 168–207. https://doi.org/10.1162/EDFP_a_00089
   PubMed Web of Science ®Google Scholar
• Wayman, J. C. (2005). Involving teachers in data-driven decision making: Using computer data systems to support teacher inquiry and reflection. Journal of Education for Students Placed at Risk (JESPAR), 10(3), 295–308. https://doi.org/10.1207/s15327671espr1003_5
   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, Article 25. https://doi.org/10.14507/epaa.v20n25.2012
   Google Scholar
• Wayman, J. C., Shaw, S., & Cho, V. (2011). Second-year results from an efficacy study of the Acuity data system.
   Google Scholar
• Wayman, J. C., Shaw, S., & Cho, V. (2017). Longitudinal effects of teacher use of a computer data system on student achievement. AERA Open. https://doi.org/10.1177/2332858416685534
   Web of Science ®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(4), 549–571. https://doi.org/10.1086/505059
   Web of Science ®Google Scholar
• Wertsch, J. V. (1991). Voices of the mind: A sociocultural approach to mediated action. Harvard University Press.
   Google Scholar
• Yin, R. K. (2017). Case study research and applications: Design and methods (6th ed.). SAGE publications.
   Google Scholar
• Young, V. M., & Kim, D. H. (2010). Using assessments for instructional improvement: A literature review. Education Policy Analysis Archives, 18, Article 19. https://doi.org/10.14507/epaa.v18n19.2010
   PubMedGoogle Scholar
• Zapata-Rivera, J. D., & Katz, I. R. (2014). Keeping your audience in mind: Applying audience analysis to the design of interactive score reports. Assessment in Education: Principles, Policy & Practice, 21(4), 442–463. https://doi.org/10.1080/0969594X.2014.936357
   Google Scholar
• Zapata-Rivera, D., Vezzu, M., & VanWinkle, W. (2013). Exploring teachers’ understanding of graphical representations of group performance (ETS Research Memorandum No. RM-13-04). Educational Testing Service.
   Google Scholar
• Zenisky, A. L., & Hambleton, R. K. (2012). Developing test score reports that work: The process and best practices for effective communication. Educational Measurement: Issues and Practice, 31(2), 21–26. https://doi.org/10.1111/j.1745-3992.2012.00231.x
   Google Scholar
• Zwick, R., Zapata-Rivera, D., & Hegarty, M. (2014). Comparing graphical and verbal representations of measurement error in test score reports. Educational Assessment, 19(2), 116–138. https://doi.org/10.1080/10627197.2014.903653
   Google Scholar