Answering the questions that count

Educational Leadership

Volumn 66, Issue 4, 2008, Pages 18-24

  Ronka, David ^a   Lachat, Mary Ann ^a   Slaughter, Rachel ^a   Meltzer, Julie ^a  

^a Public Consulting Group's   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 57449090613     PISSN: 00131784     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (12)

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2. Firestone, W. A., & Gonzalez, R. A. (2007). Culture and processes affecting data use in school districts. In P. A. Moss (Ed.), Evidence and decision making: The 106th yearbook of the National Society for the Study of Education, Part 1 (pp. 132-154). Maiden, MA: Blackwell.
3. Holcomb, E. L. (1999). Getting excited about data: How to combine people, passion, and proof. Thousand Oaks, CA:
4. Corwin Press.johnson, R. (2002). Using data to close the achievement gap: How to measure equity in our schools (1st ed.). Thousand Oaks, CA: Corwin Press.
5. Lachat, M., & Smith, S. (2004). Data use in urban high schools. Providence, RI: Education Alliance at Brown University.
6. Lachat, M., & Williams, M. (2003). Putting student performance data at lhe center of school reform. In J. DiMartino, J. Clark, & D. Wolk (Eds.), Personalized learning (pp. 210-228). Lanham, MD: Scarecrow Press.
7. Love, N., Stiles, K. E., Mundry, S,, & DiRanna, K. (2008). A data coach's guide to improving learningfor all students: Unleashing the power of collaborative inquiry. Thousand Oaks, CA: Corwin Press.
8. Mandinach, E. B., & Honey, M. (Eds.), (2008). Data-driven school improvement: Linking data and learning. New York: Teachers College Press.
9. Mieles, T., & Foley, E. (2005), From data to decisions: Lessons from school districts using data warehousing. Providence, RI: Annenberg Institute for School Reform at Brown University.
10. Rudner, L. M., & Boston, C. (2003). Data warehousing: Beyond disaggregation. Educational Leadership, 60(5), 62-65.
11. Wayman, J. C., Cho, V., & Johnston, M. T. (2007). The data-informed district: A district-wide evaluation of data use in the Natrona County School District Austin: University of Texas.
12. Wayman, J. C., Stringfield, S., & Yakimowski, M. (2004). Software enabling school improvement through analysis of student data (Report No. 67), Baltimore: Johns Hopkins University, Center for Research on the Education of Students Placed at Risk.