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Book on data science (or data analysis) in education using R
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Data Science in Education Book

  • Check the planning folder for process- and outline-related information.

  • This project started in the #dataedu Slack channel - please get in touch to join!

The Aims of This Book

Data Science in Education aims to speak to individuals working in education about data science. Particularly in educational research and data analysis settings, there does not yet exist a language for what data science is and what its potential can be. This book aims to provide a language for data science in education through both introductory and background text (i.e., what are the challenges faced by those doing data science in education; chapters 1-4). The book also involves an introduction to the R software for data analysis and a series of walkthroughs for common data and data analysis-related issues for how to address them using data science tools and techniques (chapters 5-9). Finally, the book considers what to do once one has a foundation in using data science, with discussions of how to apply and share techniques (i.e., how can one be strategic about implementing data science in educational settings?) and how educators (at the PK-12 and at the post-secondary level) can prepare others to do data science in education.


Part 1: Chapters 1-5

  • Educators who use data science techniques face unique challenges
  • It takes a lot of people in different roles to create a good learning environment. Different education roles mean different datasets, techniques, and challenges

Part 2: Chapters 6-10

  • Readers can learn and practice steps in the analysis process
  • The walk-throughs use education datasets that feel familiar to education workers. Readers can work through examples that feel more practical than commonly used datasets like mtcars or iris
  • Resources like R for Data Science, blog posts, and data science forums are part of the ongoing learning process

A note on the purpose of this section: This section can serve the same purpose as blog posts like this one. Readers can get practical instruction, then deepen their learning by reading more in-depth materials.

Part 3: Chapters 11-15

  • Now that educators are familiar with some data science techniques, it's time to tackle applying and sharing those techniques at work. This is its own skill.
  • Connect the skills taught in Part 2 to the unique challenges discussed in Part 1
  • Explore how to use programming, statistics, and content knowledge in an education workplace that is new to data science
  • Explore how educators can teach today's students to prepare them for using data in future work places

Git Issue Labels

To help contributors participate, we're using labels so folks who are new to the repo can identify tasks to participate in. If you're working on an issue, it helps us if you assign the issue to yourself so we know who to reach if we want to collaborate. The labels are:

test code: Need help running the file locally and reporting back if everything worked. If it didn't, it helps us if we have a description of what went wrong.

discussion: Sometimes we need help talking through a topic to help us make a good design choice for our readers.

help wanted: Need help getting code to run or writing a section. We'll make sure the problem we're trying to work out is clearly stated in the issue comments.

writing: New content needed. At least one author will be assigned to writing issues, but we welcome collaboration! Feel free to message the author on Slack or in the issue comments to coordinate.

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