Supporting the Initial Work of Evidence-Based Improvement Cycles Through a Data-Intensive Partnership

Bowers, Alex J.; Krumm, Andrew

Purpose: Currently in the education data use literature there is a lack of research and examples that consider the early steps of filtering, organizing, and visualizing data to inform decision making. The purpose of this study is to describe how school leaders and researchers visualized and jointly made sense of data from a common learning management system (LMS) used by students across multiple schools and grades in a charter management organization operating in the United States. To make sense of LMS data, researchers and practitioners formed a partnership to organize complex data sets, create data visualizations, and engage in joint sensemaking around data visualizations to begin to launch continuous improvement cycles.

Design: We analyzed LMS data for n=476 students in Algebra I using hierarchical cluster analysis heatmaps. We also engaged in a qualitative case study that examined the ways in which school leaders made sense of the data visualization to inform improvement efforts.

Findings: The outcome of this study is a framework for informing evidence-based improvement cycles using large, complex datasets. Central to moving through the various steps in the proposed framework are collaborations between researchers and practitioners who each bring expertise that is necessary for organizing, filtering, and visualizing data from digital learning environments and administrative data systems.

Originality: We propose an integrated cycle of data use in schools that builds on collaborations between researchers and school leaders to inform evidence-based improvement cycles.


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Also Published In

Information and Learning Sciences

More About This Work

Academic Units
Education Leadership
Published Here
October 11, 2021


Data Analysis, Data Interpretation, Data Use, Data Driven Decision Making, Evidence Based Improvement Cycles, Researcher Practitioner Partnerships, Data Science, School Leadership, Algebra I, Cluster Analysis, Case Study, Visual Data Analytics, Heatmaps

This document is a preprint of this manuscript published in the journal Information and Learning Sciences. Citation: Bowers, A.J., Krumm, A.E. (2021) Supporting Evidence-Based Improvement Cycles Through a Data-Intensive Partnership. Information and Learning Sciences, 122(9/10) 629-650.

This material is based upon work supported by the National Science Foundation under Grant No. DRL-1444621; SMA-1338487. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.