This repository is for the second edition of Data Science in Education Using R, which is a work in progress.
The authors of this text and the publisher Taylor and Francis are pleased to make Data Science in Education Using R available via bookdown at datascienceineducation.com. They request that readers access the book via the website or in print form only and do not download or reproduce copies in any other form. Any attempt to do so will be considered a contravention of the publisher’s terms of availability.
We're excited to share this book with you! You can read the current version at datascienceineducation.com. The print version of the first edition is available now through Routledge.
School districts, government agencies, and education businesses generate data at a dizzying pace. They serve it to teachers, administrators, and education consultants in a mind-boggling variety of formats. Educators and educational data practitioners want to improve the lives of students with this data. But the data is often not in a “ready-to-analyze” format. Sometimes, educators need to use high-cost proprietary systems to access and prepare data before using it.
As a result, it's hard for enthusiastic practitioners to feel a connection between research questions and the data they need to answer them. To get value from the data-deluge, some practitioners are adopting data science tools, like R.
R is an Open Source programming language for data analysis. When data science meets education, practitioners can use the information previously confined to websites and PDF reports. Teachers, administrators, and consultants can apply programming and statistics to prepare data, transform it, visualize it, and analyze it. These practices empower practitioners to answer questions that make a difference for their students.
Our book focuses on data science in education, which we define as using data science techniques to support schooling at all levels. These techniques include preparing, exploring, visualizing, and modeling data.
These techniques shouldn't be learned separately from education use cases. Using common language is important for learning practical techniques in education. We propose learning about data science through field-specific examples. Doing so will make learning more fun and meaningful.
Technology is transforming education for administrators, staff, and students. It is increasingly important for educators -- not just data analysts -- to use data to reveal the stories of their students. Our book empowers educators from elementary school to higher education to transform educational data into actionable insights. We wrote our book as a main textbook in graduate data science in education courses. We also wrote it as a practical reference for data practitioners working with education data.
By the end of this book the reader will understand:
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The diversity of data analysis skills and applications in the education field
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Unique considerations for analyzing education data
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How to run effective analysis workflows
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An increased belief in shaping data science in our education
… and the reader will be able to:
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Better define their role as a data analyst and educator
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Identify and apply solutions to education data’s unique challenges, including cleaning data and using aggregated student data
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Apply a basic analytic workflow through practice with education datasets
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Introduce data science to their workplace in a thoughtful, empathetic, and effective manner
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Introduction: Data Science in Education - You’re Invited to the Party!
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Walkthrough 1: The Education Dataset Science Pipeline With Online Science Class Data
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Walkthrough 2: Approaching Gradebook Data From a Data Science Perspective
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Walkthrough 3: Using School-Level Aggregate Data to Illuminate Educational Inequities
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Walkthrough 4: Longitudinal Analysis With Federal Students With Disabilities Data
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Walkthrough 6: Exploring Relationships Using Social Network Analysis With Social Media Data
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Walkthrough 7: The Role (and Usefulness) of Multi-Level Models
This project started in the #dataedu Slack channel. You can join the workspace here.
Community members can contribute by making changes through a pull request. We encourage community members to do their pull requests on separate branches. This helps us coordinate changes:
To help contributors participate, we use labels to organize tasks. When working on an issue, assign yourself to the issue. This helps us track the work and lets us know who to contact for more collaboration. The labels are:
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good first issue: These are requests for changes that are fun and achievable if you're new to git and GitHub -
discussion: Sometimes we need help talking through a topic or design decision. These issues won't always result in a change, but they help us clarify what's best for the final product -
test code: These issues are for running code and giving feedback about the result -
bug: These issues are for code that isn't running as expected and needs fixing -
help wanted: These issues are for general requests like help with code or writing new content -
writing: These issues are for writing new content. We will assign at least one author towritingissues -
review draft: These issues are requests to read through a draft chapter and provide feedback
If you have questions, comments, or ideas contact the authors by email at [email protected] or on Twitter:
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Emily @ebovee09
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Isabella @ivelasq3
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Joshua @jrosenberg6432
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Ryan @ry_estrellado
Until we publish the second edition, use the following citation for the first edition of the print version:
Bovee, E. A., Estrellado, R. A., Motsipak, J., Rosenberg, J. M., & Velásquez, I. C. (under contract). Data science in education using R. London, England: Routledge. Nb. All authors contributed equally. http://www.datascienceineducation.com/