Date of Award

12-2019

Degree Name

Doctor of Philosophy

Department

Educational Leadership, Research and Technology

First Advisor

Dr. Jianping Shen

Second Advisor

Dr. Patricia Reeves

Third Advisor

Dr. Brian Pyles

Keywords

Instructional change, mental models, datadriven decision-making, sense-making

Abstract

Data-driven decision making (DDDM) is a practice that has been shown to improve student achievement. However, the success of this process relies on the mental models applied during sensemaking. The purpose of this multiple case study was to describe and interpret how elementary mathematics teachers made sense of data from the Delta Math readiness screener. Additionally, this study captured the types of changes that teachers made as a result of analyzing this particular data source.

This study employed qualitative methods to determine the mental models that teachers applied during DDDM. These methods also provided participants an opportunity to describe how their instruction changed as a result of data dialogues. Primary methods of data collection were observations and semi-structured interviews. Participants were selected from a school district in Michigan that utilized the Delta Math readiness screener and provided time for teachers to analyze the data.

During the 2018-2019 school year, three fourth-grade teachers from an elementary school in southwestern Michigan began their initial implementation of the Delta Math Readiness screener. Before implementation, each participant was interviewed to better understand how they typically applied mental models to various data sources and the types of instructional change employed. The participants screened their students in the fall, winter and spring using the Delta Math Readiness screeners. Upon the conclusion of each screening, observations of the grade level data reviews were conducted, along with follow-up interviews.

This study confirmed much of the research conducted in previous studies and contributed new findings that could be useful in elevating the practice of data-driven decision making. During the 2018-2019 school year, all four mental models, including instruction, student understanding, nature of the test, and student characteristics, were applied. Mental models associated with instructional change were applied more frequently than those that are not associated with instructional change. Interestingly, this study demonstrated that a teacher could apply a mental model associated with instructional change in conjunction with a mental model not associated with instructional change and a change in instruction could still occur. Of significant interest, this study revealed a potential fifth mental model, prior instruction. This model seems to occur when teachers attribute student outcomes to the quality of instruction received in prior grade levels.

This study also provided a detailed description of the types of instructional changes employed and confirmed the four types of instructional changes articulated in the literature. Additionally, this study revealed new changes in instruction including (a) reward system, (b) reprioritization of core content, (c) strategic intervention planning, (d) more frequent formative assessment, and (e) realignment of Tier 1 and Tier 2 content.

This study has furthered the discussion around data-driven decision making by calling attention to a screener that promotes the application of mental models that are associated with instructional change. Those in K-12 leadership roles can benefit from this research, as they make decisions around the data sources that empower practitioners to engage in new techniques that bolster student achievement.

Access Setting

Dissertation-Open Access

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