Mapping a Way to Design, Implement, and Evaluate Literacy Instruction in School Settings: A Flexible Action-Oriented Data Analytic Framework

Mapping a Way to Design, Implement, and Evaluate Literacy Instruction in School Settings: A Flexible Action-Oriented Data Analytic Framework

Kouider Mokhtari (The University of Texas – Tyler, USA) and Annamary L. Consalvo (The University of Texas – Tyler, USA)
DOI: 10.4018/978-1-5225-0669-0.ch001


In this chapter, the authors present a flexible data analytic framework aimed at guiding school teams to examine assessment data and use findings to inform the design, implementation, and evaluation of literacy instruction in school settings. The data analytic tool is designed to promote teacher professional learning through teamwork and collaborative inquiry, strengthen instructional practices, and eventually improve students' literacy achievement outcomes. Following a brief review of key research findings relative to the value of data-based decision making in instructional improvement, the authors describe the data analytic framework and provide a case example which will serve to better guide school teams in using assessment data to inform the design, implementation, and evaluation of instruction for all students. Finally, the authors offer recommendations that will help support literacy coaches as they work to strengthen instructional practices and improve students' achievement outcomes within their schools and communities.
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Evidence-Based Support For The Use Of Assessment Data To Guide Instruction

Coined by Goodwin (1995), the Data Rich but Information Poor (DRIP) syndrome has been adopted by assessment experts in education. Fletcher (2013) explains this phenomenon:

This pithy acronym refers to the idea that schools now have the capability to gather an enormous amount of information about students, teachers, and innumerable aspects of school and district performance through their student information system, gradebooks, assessment systems, and other sources. They are “data rich.” But these systems rarely, if ever, talk to each other to exchange, coordinate, or integrate the data and report that integrated data in a way that is actionable by anyone. That’s what makes these same schools “information poor.” (p.31)

Instead, schools can mine available data for valuable nuggets by engaging in a critical phase of extracting patterns from assessment data using a cycle of focused questions. Once the patterns are determined, educators can name ways that the assessment information is useful and relevant to students and teachers.

The research literature related to data-based decision making indicates that while most classroom teachers and school administrators understand the role of using assessment data to guide instruction, it appears that they do not always have the supports that foster a data-driven culture in schools (Boudett & Steel, 2007; Hamilton et al., 2009; Schifter, Natarajan, Ketelhut, & Kirchgessner, 2014). Some of the reasons for this apparent lack of data use include first, a lack of access to pertinent assessment data within school systems; second, a lack of expertise in data analysis; and, third, a lack of time to review and make sense of available assessment data. Unfortunately, and too frequently, data-based decision making is not adequately supported and encouraged by school leadership. Literacy coaches, for example, report that they need material support from school administrators, in the forms of both time and resources, in addition to clearer role delineations ascertained for them by building and district leaders (Bean, Kern, Goatley, Ortleb, et al., 2015). Teachers can only take action on the assessment data pertaining to their students when these data are accessible to them, when they feel adequately prepared to use these data, and when they have the time and the conditions under which they can use these data to inform and guide their teaching (Mokhtari, Rosemary, & Edwards, 2007).

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