How Can Schools of Education Help to Build Educators’ Capacity to Use Data? A Systemic View of the Issue

Abstract

Background

With the growing emphasis for educators to use data to inform their practice, little has been done to consider the means by which the educators can acquire the requisite data literacy skills. This article provides a context for why schools of education can and must play an important role in preparing teachers to use data.

Purpose

This article sought to understand if and how schools of education are preparing teacher candidates to use data effectively or responsibly. The study examined the extent to which schools of education teach stand-alone courses on data-driven decision making or integrate data use concepts into existing courses. It also examined state licensure and certification requirements to determine if and how data use is included in documentation.

Population

A stratified randomized sample of schools of education was drawn with 208 institutions responding, representing a 25.7% response rate.

Research Design

The survey portion of the study consisted of a stratified randomized sample of all schools or departments of education in the United States. The syllabus review was a voluntary part of the survey. The licensure review was a descriptive analysis of every state's documentation for teacher licensure and certification.

Findings/Results

The survey results indicated that a vast majority of the schools of education reported that they offered a stand-alone data course, and even more integrated data use into existing courses. The syllabus review provided a deeper dive into the course offerings and indicated that the courses were more about assessment literacy than data literacy. The licensure review yielded a plethora of skills and knowledge related to data that are included in state requirements. However, there was wide variation across states in their requirements.

Conclusions

Even though schools of education reported that they are teaching about data-driven decision making in their teacher preparation programs, the results indicate that the content is more about assessment literacy than data literacy. This finding is consistent with the often observed conflation of the two constructs. Licensure requirements include both data literacy and assessment literacy, but the emphasis is more on assessment than data. With the increasing emphasis by policy makers on the importance of educators using data, it is essential that schools of education begin to incorporate data concepts into their curricula and that states make explicit the data-related skills and knowledge required for teachers for licensure and certification.

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References

Aguerrebere

(2009, August). Remarks made at the Teachers' Use of Data to Impact Teaching and Learning conference, Washington, DC.

American Recovery and Reinvestment Act of 2009 Public Law No.111-5. (2009).

Baker

E. L.

(2003). From unusable to useful assessment knowledge: A design problem (CSE Technical Report 612). Los Angeles: University of California, National Center for Research on Evaluation, Standards, and Student Testing.

Blue Ribbon Panel on Clinical Preparation and Partnerships for Improved Student Learning. (2010). Transforming teacher education through clinical practice: A national strategy to prepare effective teachers. Washington, DC: National Council for Accreditation of Teachers Education.

Choppin

(2002, April). Data use in practice: Examples from the school level. Paper presented at the meeting of the American Educational Research Association, New Orleans, LA.

Council of Chief State School Officers, Interstate Teacher Assessment and Support Consortium. (2011). InTASC model core teaching standards: A resource for state dialogue. Retrieved from http://www.ccsso.org/Documents/2011/InTASC_Model_Core_Teaching_Standards_2011.pdf

Council of Chief State School Officers. (2013, April). InTASC model core teaching standards and learning progressions for teachers 1.0. Washington, DC: CCSSO's Interstate Teacher Assessment and Support Consortium.

CCSSO Task Force on Teacher Preparation and Entry into the Profession. (2012). Our responsibility, our promise: Transforming teacher preparation and entry into the profession. Washington, DC: Council of Chief State School Officers.

Danielson

(2013). The framework for teaching. Available at http://www.danielsongroup.org

10.

Data Quality Campaign. (2012). State action 9: Educator capacity 2011 state analysis. Retrieved from http://www.dataqualitycampaign.org/stateanalysis/actions/9/.

11.

Data Quality Campaign. (2013a, April). Changing the ground game: Focus on people to improve data use at the local level. Presentation at the Changing the Ground Game conference, Crystal City, VA.

12.

Data Quality Campaign. (2013b). Right questions, right data, right answers: Data for action 2013. Washington, DC: Author.

13.

Data Quality Campaign. (2013c). State analysis by state action: State action 9. Retrieved from http://dataqualitycampaign.org/your-states-progress/10-state-actions?action=nine

14.

Data Quality Campaign. (2014, February). Teacher data literacy: It's about time. Washington, DC: Author.

15.

Data Quality Campaign Data Literacy Working Group. (2013). Definition of what it means to be a data literate educator. Washington, DC: Author.

16.

Duncan

(2009a, March 10). Federal leadership to support state longitudinal data systems. Comments made at the Data Quality Campaign Conference, Leveraging the Power of Data to Improve Education, Washington, DC.

17.

Duncan

(2009b, June 8). Secretary Arne Duncan addresses the Fourth Annual IES Research Conference. Speech made at the Fourth Annual IES Research Conference, Washington, DC. Retrieved from http://www.ed.gov/news/speeches/2009/06/06-82009.html

18.

Duncan

(2009c, May 20). Testimony before the House Education and Labor Committee. Retrieved from http://www.ed.gov/print/news/speeches/2009/05/05202009.html

19.

Duncan

(2010a, July 28). Data: The truth teller in education reform. Keynote address at Educate with Data: STATS-DC 2010, Bethesda, MD.

20.

Duncan

(2010b, November 16). Secretary Arne Duncan's remarks to National Council for Accreditation of Teacher Education. Retrieved from http://www.ed.gov/news/speeches/secretary-arne-duncans-remarks-national-council-accreditation-teacher-education

21.

Duncan

(2012, January 18). Leading education into the information age: Improving student outcomes with data. Roundtable discussion at the Data Quality Campaign National Data Summit, Washington, DC.

22.

Feldman

, & Tung

(2001). Using data-based inquiry and decision making to improve instruction. ERS Spectrum, 19(Summer), 10–19.

23.

Greenburg

, & Walsh

(2012). What teacher preparation programs teach about K-12 assessment. Washington, DC: National Council on Teacher Quality.

24.

Gummer

E. S.

, & Mandinach

E. B.

(2015). Building a conceptual framework for data literacy. Teachers College Record, 117(4).

25.

Hamilton

, Halverson

, Jackson

, Mandinach

, Supovitz

, & Wayman

(2009). Using student achievement data to support instructional decision making (NCEE 2009-4067). Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sci ences, U.S. Department of Education. Retrieved from http://ies.ed.gov/ncee/wwc/pdf/practice_guides/dddm_pg_092909.pdf

26.

Hess

F. M.

, & Kelly

A. P.

(2007). Learning to lead: What gets taught in principal-preparation programs. Teachers College Record, 109(1), 244–274.

27.

Ikemoto

G. S.

, & Marsh

J. A.

(2007). Cutting through the “data-driven” mantra: Different conceptions of data-driven decision making. In Moss

P. A.

(Ed.), Evidence and decision making: 106th yearbook of the National Society for the Study of Education: Part I (pp. 104–131). Malden, MA: Blackwell.

28.

Long

, Rivas

, Light

, & Mandinach

E. B.

(2008). The evolution of a homegrown data warehouse: TUSDStats. In Mandinach

E. B.

& Honey

(Eds.), Data-driven school improvement: Linking data and learning (pp. 209–232). New York, NY: Teachers College Press.

29.

Mandinach

E. B.

(2009, October). Data use: What we know about school-level use. Presentation at the Special Education Data Accountability Center Retreat, Rockville, MD.

30.

Mandinach

E. B.

(2012). A perfect time for data use: Using data-driven decision making to inform practice. Educational Psychologist, 47(2), 71–85.

31.

Mandinach

E. B.

, & Gummer

E. S.

(2011). The complexities of integrating data-driven decision making into professional preparation in schools of education: It's harder than you think. Alexandria, VA, Portland, OR, and Washington, DC: CNA Education, Education Northwest, and WestEd.

32.

Mandinach

E. B.

, & Gummer

E. S.

(2012). Navigating the landscape of data literacy: It IS complex. Washington, DC, and Portland, OR: WestEd and Education Northwest.

33.

Mandinach

E. B.

, & Gummer

E. S.

(2013a). Defining data literacy: A report on a convening of experts. Journal of Educational Research and Policy Studies, 13(2), 6–28. Retrieved from http://normessasweb.uark.edu/jerps/JERPS_april.pdf

34.

Mandinach

E. B.

, & Gummer

E. S.

(2013b). A systemic view of implementing data literacy into educator preparation. Educational Researcher, 42(1), 30–37.

35.

Mandinach

E. B.

, & Honey

(Eds.). (2008). Data-driven school improvement: Linking data and learning. New York, NY: Teachers College Press.

36.

Mandinach

E. B.

, & Jackson

(2012). Transforming teaching and learning through data-driven decision making. Thousand Oaks, CA: Corwin Press.

37.

Mandinach

E. B.

, Rivas

, Light

, & Heinze

(2006, April). The impact of data-driven decision making tools on educational practice: A systems analysis of six school districts. Paper presented at the meeting of the American Educational Research Association, San Francisco, CA.

38.

Marsh

J. A.

, Pane

J. F.

, & Hamilton

L. S.

(2006). Making sense of data-driven decision making in education. Santa Monica, CA: RAND Education.

39.

Mason

(2002, April). Turning data into knowledge: Lessons from six Milwaukee public schools. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA.

40.

Massell

(2001). The theory and practice of using data to build capacity: State and local strategies and their effects. In Fuhrman

S. H.

(Ed.), From the capitol to the classroom: Standards-based reform in the states (pp. 148–169). Chicago, IL: University of Chicago Press.

41.

Means

, Chen

, DeBarger

, & Padilla

(2011). Teachers' ability to use data to inform instruction: Challenges and supports. Washington, DC: U.S. Department of Education, Office of Planning, Evaluation, and Policy Development.

42.

Means

, Padilla

, & Gallagher

(2010). Use of education data at the local level: From accountability to instructional improvement. Washington, DC: U.S. Department of Education, Office of Planning, Evaluation, and Policy Development.

43.

Miller

(2009). Achieving a wealth of riches: Delivering on the promise of data to transform teaching and learning (Policy Brief). Washington, DC: Alliance for Excellent Education.

44.

North Carolina Department of Pubic Instruction. (2013). Data literacy. Retrieved from http://ites.ncdpi.wikispaces.net/Data+Literacy.

45.

Schafer

W. D.

, & Lissitz

R. W.

(1987). Measurement training for school personnel: Recommendations and reality. Journal of Teacher Education, 38(3), 57–63.

46.

U.S. Department of Education. (2009). Race to the Top program: Executive summary. Washington, DC: Author.

47.

Wise

S. L.

, Lukin

L. E.

, & Roos

L. L.

(1991). Teacher beliefs about training in testing and measurement. Journal of Teacher Education, 42(1), 37–42.