Exploratory Data Analysis
in the Classroom
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If you have been in education for more than ten minutes, you have likely heard someone expose the benefits of data-driven decision making – the idea that if you analyze your student data and use it to guide your planning processes that you will have more success. They aren’t wrong. Data-driven decision making is an important part of the continuous improvement process. It helps us monitor the impact of activities and make appropriate mid-course adjustments. There are dozens of books on the topic – this isn’t one of them.
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The problem with most books on data-driven decision making is that they don’t tell you what to actually do with your data. Most books on this topic dedicate only one chapter to the actual act of data analysis. They don’t dig deeply into the different types of data, the methods of examining data appropriately, or how to interpret the results of your analysis. That is where Exploratory Data Analysis in the Classroom comes in.
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Exploratory data analysis, or EDA, is a fluid process for examining data with an open mind. It fits well within your existing continuous improvement or data-driven decision making constructs because it is the missing piece in those existing constructs. When you perform an EDA process, you take all of the data related to your question and you squeeze all of the information out of it. Like a wet sponge, you continue to squeeze collecting more and more information about your students and how your daily activities are impacting them.
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In Exploratory Data Analysis in the Classroom, I will teach you all the skills necessary to thoroughly examine your student data and carve out meaningful insights that will inform your planning processes. I will provide step-by-step directions for performing essential data analysis tasks in spreadsheets and you will learn from the experience of a colleague through a vignette that illustrates the power of EDA.
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Exploratory Data Analysis in the Classroom is further supplemented by videos and exercises that you can work through with your team as you hone your skills and learn to love your data.
Exploratory Data Analysis in the Classroom
Introducing Ms. Newman
Exploratory Data Analysis in the Classroom utilizes a single vignette to help illustrate how a classroom teacher can benefit from the exploratory data analysis process. In this book, Ms. Newman, a fictitious seventh grade social studies teacher, has welcomed us into her classroom. With each passing chapter, you will see how Ms. Newman deploys the individual skills taught to examine the data for her incoming crop of 173 students. Readers will get a glimpse into Ms. Newman’s train of thought as she examines her data and records important take-aways to help her prepare for the new school year.
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You will also have access to a downloadable version of her actual data set so that you can follow along and practice the skills. Don’t worry – no FERPA violations here. Even though the data looks very real, the students in Ms. Newman’s classroom are made up. This data set is designed to give readers a real-world view of a student data set, warts and all.
Take a Peek Inside!
"Quality data analysis is key in the continuous improvement process. However, many educators fear data analysis and are never able to fully embrace continuous improvement because of that fear. In Exploratory Data Analysis in the Classroom Courtney has developed a step-by-step process that educators can use to establish a practical system for exploratory data analysis. This process is laid out so educators with any comfort level of data analysis can deploy the EDA process in the classroom and sustain the process over time. The system he has developed is practical, organized, and can be used individually or in a group setting. His use of vignettes, and reflection and application allow educators to build confidence in their journey of using EDA to strengthen the continuous improvement process. Exploratory Data Analysis in the Classroom is a must have for any school administrator wanting to establish a district or school level data system through professional learning communities. This resource is a priceless tool for beginning or veteran teachers to use when making data driven decisions. As a colleague, I have watched the author use EDA to strengthen the school improvement work at the Kentucky Department of Education."
Kelly A. Foster Ed.D.
Associate Commissioner
Kentucky Department of Education
What You Will Learn
Exploratory Data Analysis in the Classroom is designed to give you an understanding of the underlying theory of exploratory data analysis (EDA). This book uses existing research, step-by-step processes, and rich, reproducible vignettes to help you learn the skills necessary to complete an EDA process in your classroom. The book also includes tips, tricks, and shortcuts to speed up your process, advice on how to build your own systemic method, and additional thoughts on how the individual skills can be deployed in isolation to solve other common educational data problems. Specifically, you will learn:
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Some EDA Theory
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Basic Principles of Data
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How to summarize data
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How to measure the magnitude of change
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How to turn your observations into action research questions
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And so much more!
Exploratory Data Analysis in the Classroom uses basic spreadsheet functions and formulas to empower you to dig deeper into your student data. Throughout the book, you will learn more than 25 functions that are included in every spreadsheet package. Whether you use a Macbook, PC, or Chromebook, the universal functions taught in this book will work in your spreadsheet package of choice. Each skill is accompanied by a video that will be housed on this website so that readers can dig deeper and see the skill in action.
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Are there more efficient ways to perform some of these tasks? Sure! There is always a different way to perform a data analysis task. This book includes a chapter that reviews other popular data analysis options including pivot tables, dashboards, and other analysis tools. While these tools are great, if you don’t know how to use the essential functions discussed in this book, you will likely struggle to deploy the more rigorous tools. This book was written with your success in mind!