BernhardtV. L. (2003). No schools left behind. Educational Leadership, 60(5), 26—30.
2.
CelioM. B., and HarveyJ. (2005). Buried treasure: Developing a management guide from mountains of school data. Seattle, WA: Center on Reinventing Public Education.
3.
ChenE., HeritageM., and LeeJ. (2005). Identifying and monitoring students’ learning needs with technology. Journal of Education for Students Placed at Risk, 10(3), 309—332.
4.
ChoppinJ. (2002). Data use in practice: Examples from the school level. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA.
5.
ConfreyJ., and MakarK. (2005). Critiquing and improving data use from high stakes tests: Understanding variation and distribution in relation to equity using dynamic statistics software. In DedeC., HonanJ.P., PetersL.C., and LagemannE.C. (Eds.), Scaling up success: Lessons learned from technology-based instructional improvement (pp. 198—226). San Francisco: Jossey Bass.
6.
CoplandM. A. (2003). Leadership of inquiry: Building and sustaining capacity for school improvement. Educational Evaluation and Policy Analysis, 25(4), 375—395.
7.
DemboskyJ. W., PaneJ. F., BarneyH., and ChristinaR. (2005). Data driven decisionmaking in southwestern Pennsylvania school districts. Santa Monica, CA: RAND.
8.
DemingW. E. (1986). Out of crisis. Cambridge, MA: MIT Center for Advanced Engineering Study.
9.
FeldmanJ., and TungR. (2001). Whole school reform: How schools use the data-based inquiry and decision making process. Paper presented at the annual meeting of the American Educational Research Association, Seattle, WA.
10.
HalversonR. R., GriggJ., PrichettR., and ThomasC. (2005). The new instructional leadership: Creating data-driven instructional systems in schools. Paper presented at the annual meeting of the National Council of Professors of Educational Administration, Washington, DC.
11.
HermanJ., and GribbonsB. (2001). Lessons learned in using data to support school inquiry and continuous improvement: Final report to the Stuart Foundation. Los Angeles, CA: National Center for Research on Evaluation, Standards, and Student Testing.
12.
HolcombE. L. (2001). Asking the right questions: Techniques for collaboration and school change (2nd Ed.). Thousand Oaks, CA: Corwin.
13.
IngramD., LouisK. S., and SchroederR. G. (2004). Accountability policies and teacher decision making: Barriers to the use of data to improve practice. Teachers College Record, 106(6), 1258—1287.
14.
JuranJ. M. (1988). Juran on planning for quality. New York: Free Press.
15.
KeeneyL. (1998). Using data for school improvement: Report on the second practitioners’ conference for Annenberg Challenge sites, Houston, May 1998. Providence, RI: Annenberg Institute for School Reform.
16.
KerrK. A., MarshJ. A., IkemotoG. S., DarilekH., and BarneyH. (2006). Districtwide strategies to promote data use for instructional improvement. American Journal of Education, 112, 496—520.
17.
KoretzD. (2003). Using multiple measures to address perverse incentives and score inflation. Educational Measurement: Issues and Practice, 22(2), 18—26.
18.
LachatM. A. (2001). Data-driven high school reform: The Breaking Ranks model. Providence, RI: LAB at Brown University.
19.
LachatM. A., and SmithS. (2005). Practices that support data use in urban high schools. Journal of Education for Students Placed at Risk, 10(3), 333—349.
20.
MandinachE. B., HoneyM., and LightD. (2006). A theoretical framework for data-driven decision making. Paper presented at the annual meeting of the American Educational Research Association, San Francisco.
21.
MarshJ., KerrK., IkemotoG., DarilekH., SuttorpM. J., and ZimmerR.. (2005). The role of districts in fostering instructional improvement: Lessons from three urban districts partnered with the Institute for Learning. MG-361-WFHF. Santa Monica, CA: RAND Corporation. Retrieved December 18, 2006, from http://www.rand.org/pubs/monographs/MG361/
22.
MasonS. (2002). Turning data into knowledge: Lessons from six Milwaukee public schools. Madison, WI: Wisconsin Center for Education Research.
23.
MassellD. (2001). The theory and practice of using data to build capacity: State and local strategies and their effects. In and and FuhrmanS.H. (Ed.), From the capitol to the classroom: Standards-based reform in the states. The one-hundredth yearbook of the National Society for the Study of Education, Part II (pp. 148—169). Chicago, IL: National Society for the Study of Education.
24.
MurnaneR. J., SharkeyN. S., and BoudettK. P. (2005). Using student-assessment results to improve instruction: Lessons from a workshop. Journal of Education for Students Placed at Risk, 10(3), 269—280.
25.
PophamW. J. (1987). The merits of measurement-driven instruction. Phi Delta Kappan, 68, 679—682.
26.
PophamW. J., CruseK. I., RankinS. C., SandiferP. D., and WilliamsP. L. (1985). Measurement-driven instruction: It's on the road. Phi Delta Kappan, 66, 628—634.
27.
SchmokerM. (2004). Tipping point: From feckless reform to substantive instructional improvement. Phi Delta Kappan, 85, 424—432.
28.
SengeP. (1990). The fifth discipline: The art and practice of the learning organization. New York: Doubleday.
29.
StreiferP. A. (2002). Data-driven decision making: What is knowable for school improvement. Paper presented at the NCES Summer Data Conference, Washington, DC.
30.
SupovitzJ. A., and KleinV. (2003). Mapping a course for improved student learning: How innovative schools systematically use student performance data to guide improvement. Philadelphia: Consortium for Policy Research in Education, University of Pennsylvania Graduate School of Education.
31.
SymondsK. W. (2003). After the test: How schools are using data to close the achievement gap. San Francisco: Bay Area School Reform Collaborative.