Under both No Child Left Behind and the Every Student Succeeds Act, school leaders have been mandated to employ data-driven decision-making (DDDM) to diagnose student needs, implement targeted supports, and design school improvements. However, district administrators tasked with developing principals’ DDDM capacity face a tough road. The authors present four lessons for doing so based on lessons learned from one urban district’s year-long data dashboard intervention for their 200+ principals.
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References
1.
BowersA.J. (2017). Quantitative research methods training in education leadership and administration preparation programs as disciplined inquiry for building school improvement capacity. Journal of Research on Leadership Education, 12 (1), 72–96.
2.
BrykA.S.GomezL.M.GrunowA.LeMahieuP.G. (2015). Learning to improve: How America’s schools can get better at getting better. Cambridge, MA: Harvard Education Press.
3.
CoburnC.E.PenuelW.R.GeilK.E. (2013). Practice partnerships: A strategy for leveraging research for educational improvement in school districts. New York, NY: William T. Grant Foundation.
4.
Darling-HammondL.LaPointeM.MeyersonD.OrrM.T.CohenC. (2007). Preparing school leaders for a changing world: Lessons from exemplary leadership development programs. Stanford, CA: Stanford Educational Leadership Institute.
FinniganK.S.DalyA.J.CheJ. (2013). Systemwide reform in districts under pressure: The role of social networks in defining, acquiring, using, and diffusing research evidence. Journal of Educational Administration, 51 (4), 476–497.
7.
FullanM.Rincón-GallardoS.HargreavesA. (2015). Professional capital as accountability. Education Policy Analysis Archives, 23, 15.
8.
GoldringE.GrissomJ.A.RubinM.NeumerskiC.M.CannataM.DrakeT.SchuermannP. (2015). Make room for value added: Principals’ human capital decisions and the emergence of teacher observation data. Educational Researcher, 44 (2), 96–104.
9.
GoldringE.SchuermannP. (2009). The changing context of K-12 education administration: Consequences for Ed.D. program design and delivery. Peabody Journal of Education, 84 (1), 9–43.
10.
GrissomJ.A.RubinM.NeumerskiC.M.CannataM.DrakeT.A.GoldringE.SchuermannP. (2017). Central office supports for data-driven talent management decisions: Evidence from the implementation of new systems for measuring teacher effectiveness. Educational Researcher, 46 (1), 21–32.
11.
GroganM.AndrewsR. (2002). Defining preparation and professional development for the future. Educational Administration Quarterly, 38 (2), 233–256.
12.
GulsonK.N.WebbP.T. (2017). Mapping an emergent field of ‘computational education policy’: Policy rationalities, prediction and data in the age of Artificial Intelligence. Research in Education, 98 (1), 14–26.
13.
HonigM.I. (2012). District central office leadership as teaching: How central office administrators support principals’ development as instructional leaders. Educational Administration Quarterly, 48 (4), 733–774.
14.
HonigM.I.LortonJ.S.CoplandM.A. (2009). Urban district central office transformation for teaching and learning improvement: Beyond a zero sum game. Yearbook of the National Society for the Study of Education, 108 (1), 21–40.
15.
JohnsonS.M.MariettaG.HigginsM.C.MappK.L.GrossmanA. (2015). Achieving coherence in district improvement: Managing the relationship between the central office and schools. Cambridge, MA: Harvard Education Press.
16.
KowalskiT.J.McCordR.S.PetersonG.J.YoungP.I.EllersonN.M. (2011). The American school superintendent: 2010 decennial study. Lanham, MD: Rowan & Littlefield.
17.
PlayerD.Hambrick HittD.RobinsonW. (2014). District readiness to support school turnaround: A users’ guide to inform the work of state education agencies and districts. San Francisco, CA: Center on School Turnaround at WestEd.
18.
SpillaneJ.ReiserB.ReimerT. (2002) Policy implementation and cognition. Reframing and refocusing implementation research. Review of Educational Research, 72 (3), 387–431.
19.
ThompsonC.L.SykesG.SkrlaL. (2008). Coherent, instructionally-focused district leadership: Toward a theoretical account. East Lansing, MI: The Education Policy Center at Michigan State University.
20.
WaymanJ.C.SpikesD.D.VolonninoM.R. (2013). Implementation of a data initiative in the NCLB era. In SchildkampK.. (Eds.), Data-based decision making in education (pp. 135–153). Dordrecht: Springer.
21.
WaymanJ.C.StringfieldS.YakimowskiM. (2004). Software enabling school improvement through analysis of student data (Report No. 67). Baltimore, MD: Center for Research on the Education of Students Placed at Risk.
22.
WilliamsonB. (2017). Learning in the “platform society”: Disassembling an educational data assemblage. Research in Education, 98 (1), 59–82.