Abstract
During the past decade, data-driven decision making (DDDM) has been at the forefront of many discussions on how to improve public education in the USA. Professions such as medicine, business, politics, engineering, etc. have embraced a data culture and built tools to systematically collect and facilitate analysis of performance data, resulting in dramatic performance improvements. Every day the public depends on companies like Google that collect and aggregate data in ways that help us make decisions about everything from online purchases, to stock investments, to candidate selection. This chapter introduces current research undertaken to bring comparable advantages to education, with the goal of helping classroom- and school-level stakeholders incorporate DDDM as integral to their work. The chapter outlines several different theoretical perspectives currently applied to the DDDM challenge, including the lenses of cultural change, assessment, implementation/adoption, and technology. The bulk of the chapter focuses on research related to models of successful local DDDM implementation, including the design of technological tools and processes to facilitate collection and analysis of actionable data in ways previously not possible. The chapter concludes with implications for research and development that are relevant to those in the fields of instructional technology and learning sciences.
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References
Alavi, M., & Leidner, D. E. (2001). Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues. Management Information Systems, 25(1), 107–136.
Amrein-Beardsley, A. (2008). Methodological concerns about the education value-added assessment system. Educational Researcher, 37(2), 65–75.
Baker, R. S. J. D., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3–17.
*Bambrick-Santoyo, P. (2010). Driven by data: A practical guide to improve instruction. San Francisco, CA: Jossey-Bass.
Bernhardt, V. L. (2009). Data, data everywhere. Larchmont, NY: Eye On Education.
Birabasi, A. L. (2002). Linked: The new science of networks. Cambridge, MA: Perseus Publishing.
*Boudett, K. P., City, E. A., & Murnane, R. J. (Eds.). (2005). Data wise: A step-by-step guide to using assessment results to improve teaching and learning. Cambridge, MA: Harvard Education Press.
Boudett, K. P., & Steele, J. L. (Eds.). (2007). Data wise in action: Stories of schools using data to improve teaching and learning. Cambridge, MA: Harvard Education Press.
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.
Brunner, C., Fasca, C., Heinze, J., Honey, M., Light, D., Mandinach, E., et al. (2005). Linking data and learning: The grow network study. New York, NY: Center for Children and Technology.
*Bryk, A. S., Sebring, P. B., Allensworth, E., Luppescu, S., & Easton, J. Q. (2009). Organizing schools for improvement: Lessons from Chicago. Chicago, IL: University of Chicago Press.
Cavanagh, T. B. (2004). The new spectrum of support: Reclassifying human performance technology. Performance Improvement, 43(4), 28–32.
Chen, E., Heritage, M., & Lee, J. (2005). Identifying and monitoring students’ learning needs with technology. Journal of Education for Students Placed at Risk, 10(3), 309–332.
Chenoweth, K. (2007). It’s being done: Academic success in unexpected schools. Cambridge, MA: Harvard Education Press.
Chenoweth, K. (2009). How it’s being done: Urgent lessons from unexpected schools. Cambridge, MA: Harvard Education Press.
Cho, V., & Wayman, J. C. (2009, April). Knowledge management and educational data use. American Educational Research Association Annual Meeting. San Diego, CA
Common Core Standards Initiative. (2011a). About the standards. Retrieved from http://www.corestandards.org/about-the-standards
Common Core Standards Initiative. (2011b). The standards. Retrieved from http://www.corestandards.org/the-standards
Crawford, V. M., Schlager, M. S., Penuel, W. R., & Toyama, Y. (2008). Supporting the art of teaching in a data-rich, high-performance learning environment. In E. B. Mandinach & M. Honey (Eds.), Data-driven school improvement: Linking data and learning (pp. 109–129). New York, NY: Teachers College Press.
*Elmore, R. (2004). School reform: From the inside out. Cambridge, MA: Harvard Education Press.
Gallimore, R., Ermeling, B. A., Saunders, W. M., & Goldenberg, C. (2009). Moving the learning of teaching close to practice: Teacher education implications of school-based inquiry. The Elementary School Journal, 109(5), 537–553.
*Haney, D. (2006). Knowledge managment, organizational performance, and human performance technology. In James A. Pershing (Ed.), Handbook of human performance technology (3rd ed., pp. 619-639). San Francisco, CA: Pfeiffer.
Harris, D. N. (2011). Value-added measures in education: What every educator needs to know. Cambridge, MA: Harvard Education Press.
Hudzina, M., Rowley, K., & Wager, W. (1991). Electronic performance support technology: Defining the domain. Performance Improvement Quarterly, 9(1), 36–48.
*Kaufman, T. E., Grimm, E. D., & Miller, A. E. (2012). Collaborative school improvement: Eight practices for district-school partnerships to transform teaching and learning. Cambridge, MA: Harvard Education Press.
Kirkley, J. R., & Duffy, T. M. (1997). Designing a web-based electronic performance support system (EPSS): A case of literacy online. In B. H. Khan (Ed.), Web-based instruction (pp. 139–148). Englewood Cliffs, NJ: Educational Technology.
*Mandinach, E. B., Honey, M., & Light, D. (2012). A theoretical framework for data-driven decision making. American Educational Research Association Annual Meeting. San Francisco, CA. : Sage Publications.
Marsh, J. A., Pane, J. F., & Hamilton, L. S. (2006). Making sense of data-driven decision making in education: Evidence from recent RAND research. Santa Monica: CA. Retrieved from http://www.rand.org/pubs/occasional_papers/2006/RAND_OP170.pdf
McManus, P., & Rossett, A. (2006). Performance support tools: Delivering value when and where it is needed. Performance Improvement, 45(2), 8–16. doi:10.1002/pfi.2006.4930450204.
Means, B., Gallagher, L., & Padilla, C. (2007). Teachersʼ use of student data systems to improve instruction. U.S. Department of Education Office of Planning, Evaluation and Policy Development. Washington, D.C. Retrieved from http://www2.ed.gov/rschstat/eval/tech/teachers-data-use/teachers-data-use.pdf
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. Washington, D.C. Retrieved from http://www2.ed.gov/about/offices/list/opepd/ppss/reports.html#edtech
Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science, 5(1), 14–37.
*Picciano, A. G. (2006). Data-driven decision making for effective school leadership. Upper Saddle River, NJ: Pearson Education.
Ratner, G., & Neill, M. (2010). Common elements of successful school turnarounds: Research and experience. Retrieved from http://www.edaccountability.org/pdf/CommonElementsSuccessfulSchoolTurnarounds.pdf
Reeves, D. B. (2004). Accountability for learning: How teachers and school leaders can take charge. Alexandria, VA: Association for Supervision and Curriculum Development.
Reeves, D. B. (2010). Transforming professional development into student results. Alexandria, VA: Association for Supervision and Curriculum Development.
Senge, P. M. (1990). The fifth discipline: The art and practice of the learning organization. New York, NY: Doubleday-Currency.
Sergiovanni, T. J., & Starratt, R. J. (1998). Supervision: A redefinition (6th ed.). Boston: McGraw-Hill.
Swan, G. (2009). Tools for data-driven decision making in teacher education: Designing a portal to conduct field observation inquiry. Journal of Computing in Teacher Education, 25(3), 107–113.
Thorn, C. A. (2001). Knowledge management for educational information systems: What is the state of the field? Education Policy Analysis Archives, 9(47), 1–32.
Tucker, B. B. (2010). Putting data into practice: Lessons from New York City. Education Sector Reports. Washington, D.C. Retrieved from http://www.inpathways.net/Putting_Data_Into_Practice.pdf
U.S. Department of Education. (2009). Race to the Top Program Executive Summary, 1-15. Retrieved January 10, 2012, from http://www2.ed.gov/programs/racetothetop/executive-summary.pdf
U.S. Department of Education. (2010). U.S. Department of Education releases new report on use of data systems to support reform. Retrieved from http://www2.ed.gov/news/pressreleases/2010/01/01272010.html
Watts, D. (2003). Six degrees: The science of a connected age. New York, NY: W.W. Norton and Company.
Wayman, J. C. (2005). Guest editor’s introduction. Journal of Education for Students Placed at Risk, 10(3), 235–239.
*Wayman, J. C., & Cho, V. (2007). Preparing educators to effectively use student data systems. In T. J. Kowalski & T. J. Lasley II (Eds.), Handbook of data-based decision making in education (pp. 89–103). New York: Routledge.
Wei, R. C., Darling-Hammond, L., & Adamson, F. (2010). Professional development in the United States: Trends and challenges. Dallas, TX: National Staff Development Council.
Wenger, E. (1999). Communities of practice: Learning, meaning, and identity. Cambridge, UK: Cambridge University Press.
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Kaufman, T.E., Graham, C.R., Picciano, A.G., Popham, J.A., Wiley, D. (2014). Data-Driven Decision Making in the K-12 Classroom. In: Spector, J., Merrill, M., Elen, J., Bishop, M. (eds) Handbook of Research on Educational Communications and Technology. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3185-5_27
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