Professional Development in the Use of Data: From Data to Knowledge in Data Teams

References

• Achinstein, B. (2002). Conflict amid community: The micro-politics of teacher collaboration. Teachers College Record, 104, 421–455. doi: 10.1111/1467-9620.00168
   Web of Science ®Google Scholar
• Bertrand, M., & Marsh, J. A. (2015). Teachers’ sensemaking of data and implications for equity. American Educational Research Journal, 52(3), 423–462.
   Google Scholar
• Bolhuis, E., Schildkamp, K., & Voogt, J. (in press). Improving teacher education in the Netherlands: Datateam as learning team? European Journal of Teacher Education.
   Google Scholar
• Burke Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33(7), 14–26. doi: 10.3102/0013189X033007014
   Google Scholar
• Butler, D. L., & Schnellert, L. (2012). Collaborative inquiry in teacher professional development. Teaching and Teacher Education, 28(8), 1206–1220. doi: 10.1016/j.tate.2012.07.009
   Web of Science ®Google Scholar
• Carlgren, I. (2015). Kunskapskulturer och undervisningspraktiker [Knowledge cultures and teaching practices]. Gothenburg: Daidalos.
   Google Scholar
• Carlson, D., Borman, G., & 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(3), 378–398. doi: 10.3102/0162373711412765
   Web of Science ®Google Scholar
• Coburn, C. E., & Talbert, J. E. (2006). Conceptions of evidence use in school districts: Mapping the terrain. American Journal of Education, 112, 469–495. doi: 10.1086/505056
   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.
   Google Scholar
• Datnow, A., Park, V., & Kennedy-Lewis, B. (2013). Affordances and constraints in the context of teacher collaboration for the purpose of data use. Journal of Educational Administration, 51(3), 341–362. doi: 10.1108/09578231311311500
   Google Scholar
• Desimone, L. M. (2002). How can comprehensive school reform models be successfully implemented? Review of Educational Research, 72(3), 433–479. doi: 10.3102/00346543072003433
   Web of Science ®Google Scholar
• Earl, L. M., & Katz, S. (2006). Leading schools in a data-rich world. Harnessing data for school improvement. Thousand Oaks, CA: Corwin Press.
   Google Scholar
• Granger, R. C., & Maynard, R. (2015). Unlocking the potential of the “what works” approach to policymaking and practice improving impact evaluations. American Journal of Evaluation, 36, 558–569. 1098214015594420. doi: 10.1177/1098214015594420
   Web of Science ®Google Scholar
• Hargreaves, D. (2010). Creating a self-improving school system. Retrieved October 28, 2016, from http://dera.ioe.ac.uk/2093/1/download3Fid3D13367226filename3Dcreating-a-self-improving-school-system.pdf
   Google Scholar
• Katz, S., Earl, L., Ben Jaafar, S. (2009). Building and connecting learning communities: The power of networks for school improvement. Thousand Oaks, CA: Corwin Press.
   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 (JESPAR), 10(3), 333–349. doi: 10.1207/s15327671espr1003_7
   Google Scholar
• Lai, M. K., & Schildkamp, K. (2013). Data-based decision making: An overview. In K. Schildkamp, M. K. Lai, & L. Earl (Eds.), Data-based decision making in education: Challenges and opportunities (pp. 9–21). Dordrecht: Springer.
   Google Scholar
• Lai, M. K., Wilson, A., McNaughton, S., & Hsiao, S. (2014). Improving achievement in secondary schools: Impact of a literacy project on reading comprehension and secondary school qualifications. Reading Research Quarterly, 49(3), 305–334. doi: 10.1002/rrq.73
   Web of Science ®Google Scholar
• Leithwood, K., & Jantzi, D. (2000). Principal and teacher leadership effects: A replication. School Leadership & Management, 20(4), 415–434. doi: 10.1080/713696963
   Google Scholar
• Levin, J. A., & Datnow, A. (2012). The principal role in data-driven decision making: Using case-study data to develop multi-mediator models of educational reform. School Effectiveness and School Improvement, 23(2), 179–201. doi: 10.1080/09243453.2011.599394
   Web of Science ®Google Scholar
• Makel, M. C., & Plucker, J. A. (2014). Facts are more important than novelty replication in the education sciences. Educational Researcher, 43, 304–316. 0013189X14545513. doi: 10.3102/0013189X14545513
   Web of Science ®Google Scholar
• Mandinach, E. B., & Honey, M. (2008). Data-driven school improvement: Linking data and learning. New York, NY: Teachers College Press.
   Google Scholar
• Mandinach, E., Honey, M., Light, D., & Brunner, C. (2008). A conceptual framework for data-driven decision-making. In E. 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. (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., Pane, J. F., & Hamilton, L. S. (2006). Making sense of data-driven decision making in education: Evidence from recent RAND research (OP-170). Santa Monica, CA: RAND Corporation.
   Google Scholar
• Meijer, P. C., Verloop, N., & Beijaard, D. (2002). Multi-method triangulation in a qualitative study on teachers’ practical knowledge: An attempt to increase internal validity. Quality and Quantity, 36(2), 145–167. doi: 10.1023/A:1014984232147
   Web of Science ®Google Scholar
• Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Thousand Oaks, CA: Sage Publications.
   Google Scholar
• Nelson, T. H., & Slavit, D. (2007). Collaborative inquiry among science and mathematics teachers in the USA: Professional learning experiences through cross-grade, cross-discipline dialogue. Journal of In-Service Education, 33(1), 23–39. doi: 10.1080/13674580601157620
   Google Scholar
• Onwuegbuzie, A. J., & Leech, N. L. (2007). Validity and qualitative research: An oxymoron? Quality and Quantity, 41(2), 233–249. doi: 10.1007/s11135-006-9000-3
   Web of Science ®Google Scholar
• Park, V., & Datnow, A. (2009). Co-constructing distributed leadership: District and school connections in data-driven decision-making. School Leadership and Management, 29(5), 477–494. doi: 10.1080/13632430903162541
   Google Scholar
• Poortman, C. L., & Schildkamp, K. (2012). Alternative quality standards in qualitative research? Quality and Quantity, 46, 1727–1751. doi: 10.1007/s11135-011-9555-5
   Web of Science ®Google Scholar
• Poortman, C. L., & Schildkamp, K. (2016). School improvement effects of a data use intervention for teachers. Teaching and Teacher Education, 24, 377–386.
   Google Scholar
• Riege, A. M. (2003). Validity and reliability tests in case study research: A literature review with “hands-on” applications for each research phase. Qualitative Market Research: An International Journal, 6(2), 75–86. doi: 10.1108/13522750310470055
   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. doi: 10.1016/j.stueduc.2013.10.007
   Web of Science ®Google Scholar
• Schildkamp, K., & Kuiper, W. (2010). Data-informed curriculum reform: Which data, what purposes, and promoting and hindering factors. Teaching and Teacher Education, 26, 482–496. doi: 10.1016/j.tate.2009.06.007
   Web of Science ®Google Scholar
• Schildkamp, K., Lai, M. K., & Earl, L. (Eds.). (2013). Data-based decision making in education: Challenges and opportunities. Dordrecht: Springer.
   Google Scholar
• Schildkamp, K., & Poortman, C. L. (2015). Factors influencing the functioning of data teams. Teachers College Record, 117(5), 1–42.
   Google Scholar
• Spillane, J. P. (2012). Data in practice: Conceptualizing the data-based decision-making phenomena. American Journal of Education, 118(2), 113–141. doi: 10.1086/663283
   Web of Science ®Google Scholar
• Supovitz, J. (2010). How principals and peers influence teaching and learning. Educational Administration Quarterly, 46(1), 31–56. doi: 10.1177/1094670509353043
   Web of Science ®Google Scholar
• Timperley, H., Kaser, L., & Halbert, J. (2014). A framework for transforming learning in schools: Innovation and the spiral of inquiry: Seminar series 234. Melbourne, Victoria: Centre for Strategic Education.
   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
• Vanhoof, J., Van Petegem, P., & De Maeyer, S. (2009). Attitudes towards school self-evaluation. Studies in Educational Evaluation, 35(1), 21–28. doi: 10.1016/j.stueduc.2009.01.004
   Google Scholar
• Wayman, J. C., Jimerson, J. B., & Cho, V. (2012). Organizational considerations in establishing the data-informed district. School Effectiveness and School Improvement, 23(2), 159–178. doi: 10.1080/09243453.2011.652124
   Web of Science ®Google Scholar
• Wayman, J. C., & Stringfield, S. (2006). Data use for school improvement: School practices and research perspectives. American Journal of Education, 112(4), 463–468. doi: 10.1086/505055
   Web of Science ®Google Scholar