Skip to main content

Advertisement

Log in

Improving Instructional Practices, Policies, and Student Outcomes for Early Childhood Language and Literacy Through Data-Driven Decision Making

  • Published:
Early Childhood Education Journal Aims and scope Submit manuscript

Abstract

Since the passage of No Child Left Behind, data-driven decision making has become one of the central foci in schools in their attempt to attain and maintain adequate levels of student academic performance. The importance of early childhood education is well established with language and literacy proficiency in the early years being viewed as a leading indicator in children’s educational development. It provides schools with the initial signs of progress towards academic achievement. In this article, a conceptual framework for improving instructional practice and student outcomes in early childhood language and literacy through data-driven decision making was described. Four questions served as the structure around which the conceptual framework was built. These questions include (1) Why do data need to be collected? (2) What kinds of data need to be collected? (3) How are the data collected? (4) How are the data used for making decisions? Responses to these questions serve as tenets for guiding the decision making process.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Arter, J., & Stiggins, R. (2005). Formative assessment as assessment for learning. National Council on Measurement in Education Newsletter, 13(3), 4–5.

    Google Scholar 

  • Boston, C. (2002). The concept of formative assessment, ERIC Digest, ED470206, College Park, MD: ERIC Clearinghouse on Assessment and Evaluation. Available online at http://www.ericdigest.org/2003-3/concept.htm.

  • Chrispeels, J. H., Brown, J. H., & Castillo, S. (2000). School leader-ship teams: Factors that influence their development and effectiveness. In K. Leithwood (Ed.), Understanding schools as intelligent systems (Vol. 4 in series, Advances in research and theories of school management and educational policy) (pp. 39–73). Stamford, CT: JAI Press.

  • Fletcher, J. M., & Lyon, G. R. (1998). Reading: “A research-based approach”. In W. M. Evers (Ed.), What’s gone wrong in America’s classrooms? (pp. 49–90). Stanford, CA: Hoover Institution Press.

    Google Scholar 

  • Foley, E., Mishook, J., Thompson, J., Kubiak, M., Supovitz, J., & Rhude-Faust, M. K. (2008). Beyond test scores: Leading indicators for education. Providence, RI: Brown University, Annenberg Institute for School Reform. Available for download at www.annenberginstitute.org/WeDo/leading_indicators.php.

  • Gullo, D. F. (2005). Understanding assessment and evaluation in early childhood education (2nd ed.). New York, NY: Teachers College Press.

    Google Scholar 

  • Hamilton, L. D., Stecher, B. M., & Yuan, K. (2012). Standards-based accountability in the United States: Lessons learned and future directions. Education Inquiry, 3(2), 149–170.

    Google Scholar 

  • Haycock, K. (1998). Good teaching matters: How well-qualified teachers can close the gap. Washington, DC: Education Trust.

    Google Scholar 

  • Mandinach, E.B., Honey, M., & Light, D. (2006, April). A theoretical framework for data-driven decision making. Paper presented at the Annual Meeting of the American Educational Research Association, San Francisco, CA.

  • Marsh, J. A., Pane, J. F., & Hamilton, L. S. (2006). Making sense of data-driven decision making in education. Santa Monica, CA: The Rand Corporation. Available for download at http://www.rand.org/pubs/occasional_papers/OP170.html.

  • Massell, D. (2001). The theory and practice of using data to build capacity: State and local strategies and their effects. In S. H. Fuhrman (Ed.), From the capitol to the classroom: Standard-based reform in the states. Chicago, IL: University of Chicago Press.

    Google Scholar 

  • McCaffrey, D. F., Lockwood, J. R., Koretz, D. M., & Hamilton, L. S. (2003). Evaluating value-added models for teacher accountability. Santa Monica, CA: The Rand Corporation.

    Google Scholar 

  • Means, B., Padilla, C., DeBarger, A., & Bakia, M. (2009). Implementing data-informed decision making in schools: Teacher access, supports and use. Washington, DC: U.S. Department of Education Office of Planning, Evaluation and Policy Development.

    Google Scholar 

  • Musen, L. (2010). Early reading proficiency: Leading indicators for education. Providence, RI: Annenberg Institute for School Reform, Brown University. Available for download at www.annenberginstitute.org/WeDo/leading_indicators.php.

  • National Institute for Literacy. (2009). National reading achievement goals inform early literacy instruction. Press release. Washington, DC: National Institute for Literacy.www.nifl.gov/news/NELP01-08-09.html.

  • Rankin, L. D., & Ricchiuti, L. M. (2007). Data-driven decision making: Five questions to help make sense of your data. Classroom connect, 14(1), 4–6.

    Google Scholar 

  • Sagebrush Corporation. (2004). Data-driven decision making: A powerful tool for school improvement (A White Paper). Minneapolis, MN: Sagebrush Corporation.

    Google Scholar 

  • Schacter, J., & Jo, B. (2005). Learning when school is not in session: A reading summer day-camp intervention to improve the achievement of existing first-grade students who are economically disadvantaged. Journal of Research in Reading, 28(2), 158–169.

    Article  Google Scholar 

  • Slavin, R. E., Karweit, N. L., Wasik, B. A., Madden, N. A., & Dolan, J. L. (1994). Success for all: A comprehensive approach to prevention and early intervention. In R. E. Slavin, N. L. Karweit, & B. A. Wasik (Eds.), Preventing early school failure (pp. 175–205). Boston, MA: Allyn & Bacon.

    Google Scholar 

  • Snow, C. E., & Van Hemel, S. B. (2008). Early childhood assessment: Why, what and how. Washington, DC: The National Academies Press.

    Google Scholar 

  • Streifer, P. A. (2002). Using data to make better educational decisions. Lanham, MD: Scarecrow Press.

    Google Scholar 

  • Strickland, D., & Riley-Ayers, S. (2006). Early literacy: Policy and practice in the preschool years. New Brunswick: NJ: National Institute for Early Education Research.

    Google Scholar 

  • Wayman, J. C. (2005). Involving teachers in data-driven decision making: Using computer data systems to support teacher inquiry and reflection. Journal of Education for Students Placed at Risk, 10(3), 295–308.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dominic F. Gullo.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gullo, D.F. Improving Instructional Practices, Policies, and Student Outcomes for Early Childhood Language and Literacy Through Data-Driven Decision Making. Early Childhood Educ J 41, 413–421 (2013). https://doi.org/10.1007/s10643-013-0581-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10643-013-0581-x

Keywords

Navigation