Written by a team of education practitioners and researchers, this book introduces data science and the R programming language to people who work in education. It starts with the big questions: what a data scientist in education actually does, what to keep in mind when working with education data, and how to get started with R and RStudio. From there it covers the foundational programming skills you will use again and again.
The heart of the book is a set of eight walkthroughs, each following an education data analysis from start to finish with code you can run and adapt. They draw on sources such as online course data, gradebooks, school-level aggregate data, federal data on students with disabilities, and social media. The closing chapters look at how to bring data science tools into an education job, how to teach data science, and how to keep learning through the data science community. The book suits teachers, administrators, analysts, and education researchers who want to grow their data analysis skills.
The walkthroughs cover:
- The education data science pipeline, using online science class data
- Approaching gradebook data from a data science perspective
- Using school-level aggregate data to shed light on educational inequities
- Longitudinal analysis of federal data on students with disabilities
- Text analysis of social media data
- Social network analysis of social media data
- The role and usefulness of multilevel models
- Predicting students’ final grades with machine learning
