Mokhtari, K., Rosemary, C. A., & Edwards, P. A. (2007). Making instructional decisions based on data: What, how and why. The Reading Teacher, 61(4), 354-359.
Author: Mokhtari, K., Rosemary, C. A., & Edwards, P. A.
This article presents a framework, called the Data Analysis Framework, which will support schools in organizing, analyzing, and using various sources of data for instructional planning. Educators today are clear on the relationship between instruction and assessment, but remain largely unclear on exactly how to organize and use data effectively. Many schools have implemented school wide teams that analyze data, others include it in the literacy coach's responsibilities, while others still have no real structures in place at all. Here the authors explain that "the systematic use of data to make instructional decisions requires leadership, training, and the development of a culture of data-driven decision making and accountability" (p. 355). According to the authors, the efforts should be school-wide if they are to be effective.
The framework itself is both interesting and simple. It considers three types of data: professional development data, classroom data, and student data. The framework worksheet provides a series of questions pertaining to each type of data. Then, in a section called "Putting It All Together," the school teams are asked to find connections, strengths and needs, patterns, and implications of that data in order to develop school improvement goals and a plan of action.
In addition to presenting the framework itself, and sharing an example of one school's work, the article gives guidelines for how to implement the Data Analysis Framework. This article would be useful to a variety of educators and settings because, as the authors point out, it is easily modifiable to fit any school's specific needs. The questions could be modified, as could the types of data being analyzed. Ultimately, this article could be helpful for many educators: principals, literacy coaches, instructional coaches, math and science coaches, groups of grade level and/or content teachers, school improvement teams, district policy-makers. The point is how to analyze and use data in order to improve student achievement.
Date Added: 02/22/2008
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