RSA2: A Theoretical Framework for Data-Driven Decision Making

A Theoretical Framework for Data-Driven Decision Making

Gaining an improvement in results is the very reason that many school districts are taking part in the PLC process. (DuFour, DuFour, Eaker, Many, 2010)  Many teachers do not understand that the key to student improvement is not a magical textbook, but by making data driven decisions. By using common formative assessments, teachers are able to gain information regarding student learning. They can then process that information and make knowledgeable decisions. One of the biggest obstacles schools face is the fact that they are data rich, but information poor (Waterman, 1987).

Teachers need professional development in making data driven decisions. There is far

too much information with which teachers must deal, but the data is not easily
translatable into information and actionable knowledge. (Mandinach, Honey, Light, 2006). The online article “A Theoretical Framework for Data-Driven Decision Making” helps guide teachers in making these ever important evaluations of student learning. The article presents a framework that enables, supports, and facilitates decision making by various stakeholders. The article also discusses using technology-based tools when making decisions. Technology can help PLCs become more timely and effective.

The online resource relates to the topic of the module because both discuss creating an environment that focuses on results. Both also agree that teachers seem to have plenty of information, but they do not have the tools necessary for turning that information into something meaningful. Finally, both the module and article agree that leadership makes a major difference in the success of schools. If there is strong leadership, teachers are more likely to use data to make decisions.  A principal who is data-driven or technically savvy can exert substantial influence on the staff (Mandinach, Honey, Light, 2006).

References

DuFour, R., DuFour, R., Eaker, R., Many, T. (2010). Learning By Doing: A Handbook for Professional Learning Communities at Work. Bloomington, IN: Solution Tree Press.

Mandinach, E, Honey, M, Light, D. (2006). A Theoretical Framework for Data-Driven Decision Making. Accessed at http://www.cct.edc.org/admin/publications/speeches/DataFrame_AERA06.pdf  on November 8^th, 2012.

Waterman, R. (1987). The Renewal Factor: How the Best Get and Keep the Competitive Edge.                New York: Bantam Books.