Building a Smarter University

Description

The Big Data movement and the renewed focus on data analytics are transforming everything from healthcare delivery systems to the way cities deliver services to residents. Now is the time to examine how this Big Data could help build smarter universities. While much of the cutting-edge research that is being done with Big Data is happening at colleges and universities, higher education has yet to turn the digital mirror on itself to advance the academic enterprise. Institutions can use the huge amounts of data being generated to improve the student learning experience, enhance research initiatives, support effective community outreach, and develop campus infrastructure. This volume focuses on three primary themes related to creating a smarter university: refining the operations and management of higher education institutions, cultivating the education pipeline, and educating the next generation of data scientists. Through an analysis of these issues, the contributors address how universities can foster innovation and ingenuity in the academy. They also provide scholarly and practical insights in order to frame these topics for an international discussion.
List of Illustrations

Foreword: Building a Smarter University: Big Data, Innovation, and Ingenuity
Nancy L. Zimpher

Preface
Acknowledgments

Part I. Overview

1. Fostering Smarter Colleges and Universities: Data, Big Data, and Analytics
Jason E. Lane and B. Alex Finsel

2. Legal Issues Associated with Big Data in Higher Education: Ethical Considerations and Cautionary Tales
Jeffrey C. Sun

3. Education and… Big Data versus Big-But-Buried Data
Elizabeth L. Bringsjord and Selmer Bringsjord

Part II. Access, Completion, Success

4. Big Data’s Impact on College Admission Practices and Recruitment Strategies
Jay W. Goff and Christopher M. Shaffer

5. Who Is the Big Data Student?
Fred Fonseca and Michael Marcinkowski

6. Nudge Nation: A New Way to Use Data to Prod Students into and through College
Ben Wildavsky

7. Unanticipated Data-Driven Innovation in Higher Education Systems: From Student Success to Course Equivalencies
Taya L. Owens and Daniel J. Knox

Part III. Policy Development and Institutional Decision Making

8. Integrating Data Analytics in Higher Education Organizations: Improving Organizational and Student Success
Lisa Helmin Foss

9. The Opportunities, Challenges, and Strategies Associated with the Use of Operations-Oriented (Big) Data to Support Decision Making within Universities
John Cheslock, Rodney P. Hughes, and Mark Umbricht

10. Measuring the Internationalization of Higher Education: Data, Big Data, and Analytics
Jason E. Lane and Rajika Bhandari

11. Big Data and Human Capital Development and Mobility
Brian T. Prescott

Contributors
Index

Sujets

• Tertiary education
• Mathematics
• Science

Informations

• Publié par: State University of New York Press
• Date de parution: 30 septembre 2014
• Nombre de lectures: 0
• EAN13: 9781438454542
• Langue: English

Extrait

B UILDING A S MARTER U NIVERSITY
SUNY SERIES , C RITICAL I SSUES IN H IGHER E DUCATION
Jason E. Lane and D. Bruce Johnstone, editors
Building a Smarter University
B IG D ATA , I NNOVATION , AND A NALYTICS
Edited by Jason E. Lane
Foreword by Nancy L. Zimpher
Published by State University of New York Press, Albany
© 2014 State University of New York
All rights reserved
Printed in the United States of America
No part of this book may be used or reproduced in any manner whatsoever without written permission. No part of this book may be stored in a retrieval system or transmitted in any form or by any means including electronic, electrostatic, magnetic tape, mechanical, photocopying, recording, or otherwise without the prior permission in writing of the publisher.
For information, contact State University of New York Press, Albany, NY
www.sunypress.edu
Production, Ryan Morris
Marketing, Michael Campochiaro
Library of Congress Cataloging-in-Publication Data
Building a smarter university : big data, innovation, and analytics / edited by Jason E. Lane ; foreword by Nancy L. Zimpher.
pages cm. — (SUNY series, critical issues in higher education)
Includes bibliographical references and index.
ISBN 978-1-4384-5453-5 (hardcover : alk. paper)
ISBN 978-1-4384-5452-8 (pbk. : alk. paper)
ISBN 978-1-4384-5454-2 (ebook) 1. Education, Higher—United States—Data processing—Congresses. I. Lane, Jason E., editor of compilation.
LB2395.7.B85 2015
378.00285—dc23
2014008138
10 9 8 7 6 5 4 3 2 1
To Kari and Emerson:
You have shown me the true meaning of life.
C ONTENTS
List of Illustrations
Foreword: Building a Smarter University: Big Data, Innovation, and Ingenuity
N ANCY L. Z IMPHER
Preface
Acknowledgments
Part I. Overview
Chapter 1. Fostering Smarter Colleges and Universities: Data, Big Data, and Analytics
J ASON E. L ANE AND B. A LEX F INSEL
Chapter 2. Legal Issues Associated with Big Data in Higher Education: Ethical Considerations and Cautionary Tales
J EFFREY C. S UN
Chapter 3. Education and … Big Data versus Big-But-Buried Data
E LIZABETH L. B RINGSJORD AND S ELMER B RINGSJORD
Part II. Access, Completion, Success
Chapter 4. Big Data’s Impact on College Admission Practices and Recruitment Strategies
J AY W. G OFF AND C HRISTOPHER M. S HAFFER
Chapter 5. Who Is the Big Data Student?
F RED F ONSECA AND M ICHAEL M ARCINKOWSKI
Chapter 6. Nudge Nation: A New Way to Use Data to Prod Students into and through College
B EN W ILDAVSKY
Chapter 7. Unanticipated Data-Driven Innovation in Higher Education Systems: From Student Success to Course Equivalencies
T AYA L. O WENS AND D ANIEL J. K NOX
Part III. Policy Development and Institutional Decision Making
Chapter 8. Integrating Data Analytics in Higher Education Organizations: Improving Organizational and Student Success
L ISA H ELMIN F OSS
Chapter 9. The Opportunities, Challenges, and Strategies Associated with the Use of Operations-Oriented (Big) Data to Support Decision Making within Universities
J OHN C HESLOCK , R ODNEY P. H UGHES , AND M ARK U MBRICHT
Chapter 10. Measuring the Internationalization of Higher Education: Data, Big Data, and Analytics
J ASON E. L ANE AND R AJIKA B HANDARI
Chapter 11. Big Data and Human Capital Development and Mobility
B RIAN T. P RESCOTT
Contributors
Index
I LLUSTRATIONS Figure 3.1 Floridi’s ontology of information Figure 3.2 BD vs. B 3 D Figure 3.3 B 3 D-based representation of a limit in seventhgrade math Table 3.1 Zeno’s framework for the Paradox of the Arrow Figure 4.1 Median distance from enrolled students’ home to college by ACT composite score, 2011 Figure 4.2 Enrollment of ACT-tested students by college choice number and composite score, 2011 Figure 4.3 Average number of times ACT-tested students with contact data release were selected by first testing and ACT composite score, 2011 Figure 4.4 Full-time student enrollment at four-year colleges and universities by tuition and fee level Figure 4.5 ERP and CRP Independent and Integrated Operations Models Figure 4.6 EFM Model for Engaging Big Data in College and University Admission Practices Figure 7.1 Two-way relationship between instrument types Figure 7.2 From student success to course equivalencies Figure 7.3 Integrated data systems: students, programs, courses, and systems Figure 8.1 Five stages in the innovation process in organizations Table 8.1 Factors that influence individual adoption of data analytics Table 8.2 Which statement best describes the status of data analytics at your institution? Table 8.3 Individual usage of data analytics during the last year Table 8.4 How do you anticipate your usage of data analytics will change in the next year? Figure 8.2 Model of the individual adoption of data analytics at higher education institutions Table 9.1 Alternative Reporting Structures for Courses Table 10.1 Sources of international student mobility data for Project Atlas Figure 11.1 Unpacking the sources of missing data in UI wage record files in Washington State Figure 11.2 The availability of individual-level data depends on students’ post-secondary educational pathways in Oregon Table 11.1 Employment outcomes and mobility of recent college graduates Table 11.2 Retention and recruitment of recent college graduates for employment
F OREWORD
Building a Smarter University Big Data, Innovation, and Ingenuity
NANCY L. ZIMPHER
T his volume, the third in SUNY’s Critical Issues in Higher Education series, is, like those that came before it, a companion piece to a conference—this one entitled, Building a Smarter University.
On October 29 and 30, 2013, in New York City, SUNY brought together hundreds of great minds from across several sectors—education, business, technology—to dive deep into one of the hottest topics of the day: Big Data. Specifically, for those of us at the conference and those who have contributed to this volume, the task was to plumb the possibilities of the 21st-century data explosion and come to a more intimate understanding of how data—Big Data—can be used to enhance education. Or, to put a finer point on it, we set about exploring the question: How can we harness the untold power of the ever-swelling ocean of data and use it, purposefully, to build smarter, more innovative, resourceful, and effective universities that fully meet the needs of an increasingly complex society?
With the help of our cosponsors, this year’s conference gathered more than 400 participants from 25 states, Jamaica, and Mexico; 50 of our SUNY campuses; and 42 other colleges and universities. It included an impressive slate of 65 speakers and panels, which together sparked groundbreaking conversation on subjects like “How Will Big Data Transform Higher Education?,” “Tapping Big Data to Strengthen the Education Pipeline,” “The Cautionary Side of Big Data,” and “Data Scientist: The Sexiest Job of the 21st Century.” We were also thrilled to introduce at the conference the inaugural class of SUNY Big Data Fellows, eight future leaders in data science from across multiple disciplines who are demonstrating the great potential of data usage in a range of fields, from psychology to finance to special education to nursing to applied mathematics.
But for all this “Big” talk, there was a rather unexpected moment at the conference when our keynote speaker, Harper Reed, cried foul at the term Big Data , the very subject he was there to discuss. The “Big,” he asserted, is unnecessary, little more than a catchphrase meant to dress up a generic word. His deeper point, he went on to explain, was that “Big” doesn’t describe a new kind of data or even a new phenomenon, that data are data are data, big or little. If anything, he argued, the adjective underscores new potential in collection, interpretation, and application of the volume of data that we are amassing daily and that the world is only beginning to understand.
To a point, I must beg to differ with Harper Reed on this one. The virtual tsunami of data created by technology as a by-product of the tools we now, rather suddenly, use in our everyday lives, is something entirely new, and learning what to do with it all—how to capture, share, store, manage, interpret, analyze, and, in effect, use it all—is our collective charge. The 2012 touchstone book The Human Face of Big Data reported that we now produce every two days the amount of data produced by all of human kind from the dawn of civilization until 2003—and the pace is accelerating. 1 If that’s not big, I don’t know what is.
But I fully agree with Harper’s observation about potential. In terms of getting to know ourselves and how we tick, humanity is on the edge of something incalculably vast, something that will change us as a species, change how we move forward into the world and into the future. We owe it to ourselves to deeply and thoughtfully explore the possibilities of data and learn to wring from them as much lifeenhancing information as possible.
The use of data to predict patterns and trends, to build better businesses, is, of course, nothing new. We each deepen our digital footprint with every credit card swipe, text, Tweet, and status update. All of this amounts to billions of data points we are putting out there for the world to use while hardly making an effort. Corporations are learning how to harness these data to transform the customer experience. If you shop at Amazon, you are prompted before checkout to look at a few other select items based on your pending purchase and similar buys by other customers. Netflix suggests movies or television series’ you