Data Science in Education Using R

305 Pages·2021·6.56 MB·english

by  Ryan A. Estrellado

Data Science in
Education Using R
Data Science in Education Using R is the go-to reference for learning data science in
the education field. The book answers questions like: What does a data scientist in
education do? How do I get started learning R, the popular open-source statistical
programming language? And what does a data analysis project in education look
like?
If you’re just getting started with R in an education job, this is the book you’ll
want with you. This book gets you started with R by teaching the building blocks
of programming that you’ll use many times in your career. The book takes a “learn
by doing” approach and offers eight analysis walkthroughs that show you a data
analysis from start to finish, complete with code for you to practice with. The book
finishes with how to get involved in the data science community and how to inte-
grate data science in your education job.
This book will be an essential resource for education professionals and research-
ers looking to increase their data analysis skills as part of their professional and
academic development.
Ryan A. Estrellado has served public schools for over seventeen years as a school
psychologist and administrator. Ryan writes about working with data, education
and other projects at https://ryanestrellado.com.
Emily A. Freer is the Director of Educational Development and Assessment
at the Marquette University School of Dentistry. Learn more about Emily at
https:// emilyfreer.com.
Jesse Mostipak is a Community Advocate for Kaggle, and has worked both as a
high school science teacher and lead data scientist within education non-profits.
To follow Jesse’s learning adventures in Python and deep learning, head over to
https://jessemaegan.com.
Joshua M. Rosenberg is an Assistant Professor of STEM Education at the University
of Tennessee, Knoxville. Follow Josh’s work at http://joshuamrosenberg.com.
Isabella C. Velásquez is a data analyst committed to nonprofit work with the goal
of reducing racial and socioeconomic inequities. To follow Isabella’s personal pro-
jects, join her at https://ivelasq.rbind.io.
“The authors have provided the definitive guide to the topic. The combination of
theory and hands-on practical tutorials make this an invaluable resource for the
growing fields of learning analytics and educational data science”.
—Mark Warschauer, Professor of Education and Informatics at the
University of California, Irvine
“This book is a clear, compelling guide for real-world practitioners who are ready
to use modern tools of data science in the education domain. The effective data
analysis content would benefit almost anyone getting started with data today, but
these authors' thoughtful, focused handling of the specific issues involved in work-
ing with education data sets it apart from most introductory data science books”.
—Julia Silge, Software Engineer at RStudio
“There are many resources for learning how to analyze education data. But what
has long been missing is an inclusive and pedagogically refined resource on how
to leverage modern data science principles, workflows, and tools. Data Science in
Education Using R fills this massive gap and more. It will be the go to resource for
the next generation of data driven education professionals. And is a beautiful expo-
sition of how to responsibly work with data from the real, messy, world”.
—Dustin Tingley, Deputy Vice Provost for Advances in Learning at
Harvard University
“Data Science in Education Using R provides a wide menu of resources for individ-
uals in education who wish to both learn R and think more deeply about their
relationship to data. The book provides both practical guidance through worked
examples and discussions of relevant theory and past research. That the book is
written by authors who have varied research and practice positions lends itself to
its broad-ranging appeal”.
—Teomara (Teya) Rutherford, Assistant Professor of Learning
Sciences, University of Delaware
“Doing data science in education is an interdisciplinary endeavor. If you are a
teacher, an administrator, or an educational researcher who’s looking for a theory-
informed, practice-oriented, and equity-minded introduction to educational data
science, no need to look further than this book. It will help you unleash your data
science ‘superpower,’ turbocharge your practice, and make real-world changes in
your organization”.
—Bodong Chen, Associate Professor and Co-Director of Learning
Informatics Lab at the University of Minnesota
Data Science in
Education Using R
Ryan A. Estrellado, Emily A. Freer,
Jesse Mostipak, Joshua M. Rosenberg
and Isabella C. Velásquez
First published 2021
by Routledge
2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN
and by Routledge
52 Vanderbilt Avenue, New York, NY 10017
Routledge is an imprint of the Taylor & Francis Group, an informa business
© 2021 Ryan A. Estrellado, Emily A. Freer, Jesse Mostipak, Joshua M. Rosenberg
and Isabella C. Velásquez
The right of Ryan A. Estrellado, Emily A. Freer, Jesse Mostipak, Joshua M.
Rosenberg and Isabella C. Velásquez to be identified as authors of this work
has been asserted by them in accordance with sections 77 and 78 of the
Copyright, Designs and Patents Act 1988.
All rights reserved. No part of this book may be reprinted or reproduced or
utilised in any form or by any electronic, mechanical, or other means, now
known or hereafter invented, including photocopying and recording, or in
any information storage or retrieval system, without permission in writing
from the publishers.
Trademark notice: Product or corporate names may be trademarks or registered
trademarks, and are used only for identification and explanation without
intent to infringe.
British Library Cataloguing-in-Publication Data
A catalogue record for this book is available from the British Library
Library of Congress Cataloging-in-Publication Data
Names: Estrellado, Ryan A., author.
Title: Data science in education using R / Ryan A. Estrellado, Emily A.
Freer, Jesse Mostipak, Joshua M. Rosenberg, Isabella C. Velásquez.
Description: Abingdon, Oxon; New York, NY: Routledge, 2021. |
Includes bibliographical references and index.
Identifiers: LCCN 2020019952 (print) | LCCN 2020019953 (ebook) |
ISBN 9780367422240 (hardback) | ISBN 9780367422257 (paperback) |
ISBN 9780367822842 (ebook)
Subjects: LCSH: Educational statistics—Data processing. | Educational
evaluation—Data processing. | R (Computer program language)
Classification: LCC LB2846 .E78 2021 (print) |
LCC LB2846 (ebook) | DDC 370.72/7—dc23
LC record available at https://lccn.loc.gov/2020019952
LC ebook record available at https://lccn.loc.gov/2020019953
ISBN: 978-0-367-42224-0 (hbk)
ISBN: 978-0-367-42225-7 (pbk)
ISBN: 978-0-367-82284-2 (ebk)
Typeset in Stone Serif
by codeMantra
Ryan:
To my wife, Lucy, and my sons, Dylan and Adam, for enduring so
much typing during dinner. And to Dan Winters, for enduring so
many plots over coffee.
Emily:
To my husband, Dan, who supports me every day and has
believed in this book from day one. To my family and to Gus, who
accompanied me on the journey.
Jesse:
To Leo, Miles, Abby, and Jinx.
Josh:
To Katie and Jonah and to Teri, Joel, Aaron, and Jess, for supporting
and encouraging me (in different ways!) from the start to the finish
of writing this book.
Isabella:
To my loving family: to my parents, for giving me everything. To my
older brother Gustavo E., for never telling me to go read the manual.
To my younger brother Gustavo A., for inspiring me to be the best
programmer I can be. To Kitty, for her critical eye (and purrs).

Contents
Acknowledgments ix
Preface xi
1 Introduction: Data science in education—you’re
invited to the party! 1
2 How to use this book 5
3 What does data science in education look like? 13
4 Special considerations 21
5 Getting started with R and RStudio 27
6 Foundational skills 39
7 Walkthrough 1: The education data science pipeline
with online science class data 61
8 Walkthrough 2: Approaching gradebook data from
a data science perspective 93
9 Walkthrough 3: Using school-level aggregate data to
illuminate educational inequities 109
10 Walkthrough 4: Longitudinal analysis with federal
students with disabilities data 127
11 Walkthrough 5: Text analysis with social media data 163
12 Walkthrough 6: Exploring relationships using social
network analysis with social media data 179
13 Walkthrough 7: The role (and usefulness) of multilevel models 191
14 Walkthrough 8: Predicting students’ final grades
using machine learning methods with online course data 205
viii ConTEnTS
15 Introducing data science tools to your education job 229
16 Teaching data science 241
17 Learning more 251
18 Additional resources 257
19 Conclusion: Where to next? 263
Appendices 269
References 281
Index 289
Acknowledgments
This work was supported by many individuals from the DataEdu Slack channel
(https://dataedu.slack.com/). Thank you to everyone who contributed code, sug-
gested changes, asked questions, filed issues, and even designed a logo for us:
Daniel Anderson, Abi Aryan, Jason Becker, William Bork, Jon Duan, Erin Grand,
Ellis Hughes, Ludmila Janda, Jake Kaupp, Kasia Banas, Nathan Kenner, Zuhaib
Mahmood, David Ranzolin, Kris Stevens, Bret Staudt Willet, and Gustavo Velásquez.
Thank you to the data scientists in education that took time to share their
stories with us: Isabella Fante, LaCole Foots, Tobie Irvine, Arpi Karapetyan, John
LaPlante, and Andrew Morozov.
Thank you to the editor of this book at Routledge, Hannah Shakespeare. We
appreciated Hannah’s incisive, constructive feedback, interest, and support for the
book and our unique approach to writing it—one which involved writing the book
“in the open” (through GitHub) and sharing it on a freely available website.