Data-based decision making in teams: enablers and barriers

References

• Achinstein, B. (2002). Conflict amid community: The micropolitics of teacher collaboration. Teachers College Record, 104(3), 421–455. Retrieved from http://www.tcrecord.org doi: 10.1111/1467-9620.00168
   Web of Science ®Google Scholar
• Birenbaum, M. (2014). Conceptualizing assessment culture in school. In C. Wyatt-Smith, V. Klenowski, & P. Colbert (Eds.), Designing assessment for quality learning (pp. 285–302). Dordrecht, The Netherlands: Springer.
   Google Scholar
• Blaich, C., & Wise, K. (2011). From gathering to using assessment results: Lessons from the Wabash national study (NILOA Occasional Paper No. 8). Champaign, IL: National Institute for Learning Outcomes Assessment (NILOA), University of Illinois at Urbana-Champaign.
   Google Scholar
• Bolhuis, E. D., Schildkamp, K., & Voogt, J. M. (2016). Improving higher education in the Netherlands: Data team as a dream team. European Journal of Teacher Education, 39, 320–339. doi:10.1080/02619768.20161171313
   Web of Science ®Google Scholar
• Butler, D. L., & Schnellert, L. (2012). Collaborative inquiry in teacher professional development. Teaching and Teacher Education, 28, 1206–1220. doi:10.1016/j.tate.2012.07.009
   Web of Science ®Google Scholar
• Coburn, C. E., & Turner, E. O. (2011). Research on data use: A framework and analysis. Measurement: Interdisciplinary Research & Perspective, 9, 173–206. doi:10.1080/15366367.2011.626729
   Google Scholar
• Cohen, L., Manion, L., & Morrison, K. (2007). Research methods in education (6th ed.). London, UK: Routledge.
   Google Scholar
• Collinson, V., Kozina, E., Lin, Y.-H., Ling, L., Matheson, I., Newcombe, L., & Zogla, I. (2009). Professional development for teachers: A world of change. European Journal of Teacher Education, 32, 3–19. doi:10.1080/02619760802553022
   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, 114(11), 1–38. Retrieved from http://www.tcrecord.org
   PubMed Web of Science ®Google Scholar
• Datnow, A., & Hubbard, L. (2014, April). Teachers’ use of assessment data to inform instruction: Lessons from the past and prospects for the future. Paper presented at the Annual Meeting of the American Educational Research Association, Philadelphia, PA.
   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, 341–362. doi:10.1108/09578231311311500
   Google Scholar
• D’Mello, S., Lehman, B., Pekrun, R., & Graesser, A. (2014). Confusion can be beneficial for learning. Learning and Instruction, 29, 153–170. doi:10.1016/j.learninstruc.2012.05.003
   Web of Science ®Google Scholar
• Downey, C., & Kelly, A. (2013). Professional attitudes to the use of data in England. In K. Schildkamp, M. K. Lai, & L. Earl (Eds.), Data-based decision making in education: Challenges and opportunities (pp. 69–89). Dordrecht, The Netherlands: Springer.
   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
• Earley, P., & Bubb, S. (2014). Data and inquiry driving school improvement: Recent developments in England. Journal of Educational, Cultural and Psychological Studies, 9, 167–184. doi:10.7358/ecps-2014-009-earl
   Google Scholar
• Festinger, L. (1957). A theory of cognitive dissonance. Stanford, CA: Stanford University Press.
   Google Scholar
• Fiore, S. M., Smith-Jentsch, K. A., Salas, E., Warner, N., & Letsky, M. (2010). Towards an understanding of macrocognition in teams: Developing and defining complex collaborative processes and products. Theoretical Issues in Ergonomics Science, 11, 250–271. doi:10.1080/14639221003729128
   Google Scholar
• Griffiths, V., Thompson, S., & Hryniewicz, L. (2014). Landmarks in the professional and academic development of mid-career teacher educators. European Journal of Teacher Education, 37, 74–90. doi:10.1080/02619768.2013.825241
   Web of Science ®Google Scholar
• Grossman, P., Wineburg, S., & Woolworth, S. (2001). Toward a theory of teacher community. Teachers College Record, 103(6), 942–1012. Retrieved from http://www.tcrecord.org doi: 10.1111/0161-4681.00140
   Web of Science ®Google Scholar
• Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and techniques (3rd ed.). San Francisco, CA: Morgan Kaufmann.
   Google Scholar
• Hand, D. J. (2009). Inquiry teams: Bridging data-informed decision making and teacher inquiry (Doctoral dissertation). Available from ProQuest Dissertations & Theses – Gradworks database. (UMI No. 3374179).
   Google Scholar
• Henry, S. F. (2010, April/May). Talk that talk. Paper presented at the Annual Meeting of the American Educational Research Association, Denver, CO.
   Google Scholar
• Henry, S. F. (2012). Instructional conversations: A qualitative exploration of differences in elementary teachers’ team discussions (Doctoral dissertation). Available from ProQuest Dissertations & Theses – Gradworks database. (UMI No. 3535056).
   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), 1–31. doi:10.14507/epaa.v22n52.2014
   Google Scholar
• Jacobs, J., Gregory, A., Hoppey, D., & Yendol-Hoppey, D. (2009). Data literacy: Understanding teachers’ data use in a context of accountability and response to Intervention. Action in Teacher Education, 31(3), 41–55. doi:10.1080/01626620.2009.10463527
   Google Scholar
• Johnson, K., Greenseid, L. O., Toal, S. A., King, J. A., Lawrenz, F., & Volkov, B. (2009). Research on evaluation use: A review of the empirical literature from 1986 to 2005. American Journal of Evaluation, 30, 377–410. doi:10.1177/1098214009341660
   Web of Science ®Google Scholar
• Katz, S., & Dack, L. A. (2014). Towards a culture of inquiry for data use in schools: Breaking down professional learning barriers through intentional interruption. Studies in Educational Evaluation, 42, 35–40. doi:10.1016/j.stueduc.2013.10.006
   Web of Science ®Google Scholar
• Katz, S., Earl, L., Ben Jaafar, S., Elgie, S., Foster, L., Halbert, J., & Kaser, L. (2008). Learning networks of schools: The key enablers of successful knowledge communities. McGill Journal of Education, 43, 111–137. Retrieved from http://mje.mcgill.ca/index doi: 10.7202/019578ar
   Google Scholar
• Katz, S., Sutherland, S., & Earl, L. (2005). Toward an evaluation habit of mind: Mapping the journey. Teachers College Record, 107(10), 2326–2350. Retrieved from http://www.tcrecord.org doi: 10.1111/j.1467-9620.2005.00594.x
   Web of Science ®Google Scholar
• Kezar, A. (2013). Institutionalizing student outcomes assessment: The need for better research to inform practice. Innovative Higher Education, 38, 189–206. doi:10.1007/s10755-012-9237-9
   Google Scholar
• Krüger, M., & Geijsel, F. (2011, April). The effect of school leader on inquiry habit of mind. Paper presented at the Annual Meeting of the American Educational Research Association, New Orleans, LA.
   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. doi:10.1207/s15327671espr1003_7
   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. doi:10.1016/j.stueduc.2013.12.006
   Web of Science ®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, The Netherlands: 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, 305–334. doi:10.1002/rrq.73
   Web of Science ®Google Scholar
• Lewins, A., & Silver, C. (2007). Using software in qualitative research: A step-by-step guide. London, UK: Sage.
   Google Scholar
• Little, J. W. (2012). Understanding data use practice among teachers: The contribution of micro-process studies. American Journal of Education, 118, 143–166. doi:10.1086/663271
   Web of Science ®Google Scholar
• Love, N., Stiles, K. E., Mundry, S., & DiRanna, K. (2008). The data coach’s guide to improving learning for all students. Thousand Oaks, CA: Corwin.
   Google Scholar
• Mandinach, E., Friedman, J. M., & Gummer, E. (2015). How can schools of education help to build educators’ capacity to use data? A systemic view of the issue. Teachers College Record, 117(4), 1–50. Retrieved from http://www.tcrecord.org
   Web of Science ®Google Scholar
• Mandinach, E. B., & Gummer, E. S. (2012). Navigating the landscape of data literacy: It IS complex. San Francisco, CA: WestEd and Education Northwest.
   Google Scholar
• Mandinach, E. B., Honey, M., & Light, D. (2006, April). A theoretical framework for data-driven decision making. Paper presented at the Annual Meeting of the American Educational Research Association, San Francisco, CA.
   Google Scholar
• McCann, C., & Kabaker, J. C. (2013). Promoting data in the classroom: Innovative state models and missed opportunities. Retrieved from https://static.newamerica.org/attachments/2316-promoting-data-in-the-classroom/Promoting%20Data_FINAL_FOR_RELEASE_0.be24efafdd944b70a63d8ee1b27729b4.pdf
   Google Scholar
• Miles, M. B., & Huberman, Michael, A. (1994). Qualitative data analysis: An expanded sourcebook (2nd ed.). Thousands Oaks, CA: Sage.
   Google Scholar
• Ministerie van Onderwijs, Cultuur en Wetenschap. (2011). Kwaliteit in verscheidenheid: Strategische agenda hoger onderwijs, onderzoek en wetenschap [Quality in variety: Strategical agenda higher education and research]. Den Haag, The Netherlands: Author.
   Google Scholar
• Nelson, T. H. (2009). Teachers’ collaborative inquiry and professional growth: Should we be optimistic? Science Education, 93, 548–580. doi:10.1002/sce.20302
   Web of Science ®Google Scholar
• Nelson, T. H., Deuel, A., Slavit, D., & Kennedy, A. (2010). Leading deep conversations in collaborative inquiry groups. The Clearing House: A Journal of Educational Strategies, Issues and Ideas, 83, 175–179. doi: 10.1080/00098650903505498
   Google Scholar
• Nelson, T. H., Slavit, D., & Deuel, A. (2012). Two dimensions of an inquiry stance toward student-learning data. Teachers College Record, 114(8), 1–42. Retrieved from http://www.tcrecord.org
   Web of Science ®Google Scholar
• Organisation for Economic Co-operation and Development. (2013). Synergies for better learning: An international perspective on evaluation and assessment. Paris, France: Author.
   Google Scholar
• Poortman, C. L., & Schildkamp, K. (2012). Alternative quality standards in qualitative research? Quality & Quantity, 46, 1727–1751. doi:10.1007/s11135-011-9555-5
   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. (2013). Conclusions and a data use framework. In K. Schildkamp, M. K. Lai, & L. Earl (Eds.), Data-based decision making in education: Challenges and opportunities (pp. 177–191). Dordrecht, The Netherlands: Springer.
   Google Scholar
• Schildkamp, K., & Poortman, C. (2015). Factors influencing the function of data teams. Teacher College Record, 117(4). Retrieved from http://www.tcrecord.org
   Web of Science ®Google Scholar
• Selart, M. (2005). Understanding the role of locus of control in consultative decision-making: A case study. Management Decision, 43, 397–412. doi:10.1108/00251740510589779
   Google Scholar
• Shaw, S. M., Wayman, J. C., & Svinicki, M. D. (2011). The relationship between teacher efficacy and teacher response to data-driven instructional reform. Austin TX: University of Texas.
   Google Scholar
• Slavit, D., Nelson, T. H., & Deuel, A. (2013). Teacher groups’ conceptions and uses of student-learning data. Journal of Teacher Education, 64, 8–21. doi:10.1177/0022487112445517
   Web of Science ®Google Scholar
• Stokes, L. (2001). Lessons from an inquiring school: Forms of inquiry and conditions for teacher learning. In A. Lieberman & L. Miller (Eds.), Teachers caught in the action: Professional development that matters (pp. 141–158). New York, NY: Teachers College Press.
   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. Philadelphia, PA: Consortium for Policy Research in Education.
   Google Scholar
• Swennen, A., Jones, K., & Volman, M. (2010). Teacher educators: Their identities, sub-identities and implications for professional development. Professional Development in Education, 36, 131–148. doi:10.1080/19415250903457893
   Google Scholar
• Wayman, J. C., & Jimerson, J. B. (2014). Teacher needs for data-related professional learning. Studies in Educational Evaluation, 42, 25–34. doi:10.1016/j.stueduc.2013.11.001
   Web of Science ®Google Scholar
• Weinstein, M. (2013). TAMSAnalyzer (Version 4.45b7ahl). Kent, OH: Kent State University.
   Google Scholar
• Williams, J. (2011). A comparison of secondary principals’ use of data systems to increase student achievement in mathematics (Doctoral dissertation). Available from ProQuest LCC database. (UMI No. 3453631)
   Google Scholar
• Yin, R. K. (2014). Case study research: Design and methods (5th ed.). London, UK: Sage.
   Google Scholar