Building a smarter university : big data, innovation, and analytics / edited by Jason E. Lane ; foreword by Nancy L. Zimpher.

Format:

Book

Author/Creator:

Zimpher, Nancy L., author of introduction, etc.

Contributor:

Lane, Jason E., editor.

Series:

SUNY series, critical Issues in higher education.

SUNY series, Critical Issues in Higher Education

Language:

English

Subjects (All):

Education, Higher--United States--Data processing--Congresses.

Education, Higher.

Physical Description:

1 online resource (344 pages) : illustrations.

Edition:

1st ed.

Place of Publication:

Albany, New York : SUNY Press, 2014.

Language Note:

English

Summary:

Demonstrates how universities can use Big Data to enhance operations and management, improve the education pipeline, and educate the next generation of data scientists.

Contents:

Intro

Contents

List of Illustrations

Foreword: Building a Smarter University: Big Data, Innovation, and Ingenuity

Note

Reference

Preface

Acknowledgments

Part I.Overview

Chapter 1: Fostering Smarter Colleges and Universities: Data, Big Data, and Analytics

Abstract

Unpacking Big Data

Data Analytics, Data Mining, and Machine Learning

Big Data and Student Success

Big Data and Research

Big Data and Education: Creating Data Scientists

Higher Education Systems: Taking Data Analytics to Scale

The Cautionary Side

The Way Forward

Notes

References

Chapter 2: Legal Issues Associated with Big Data in Higher Education: Ethical Considerations and Cautionary Tales

Privacy and Education Records

Intellectual Property

Security

Recommendations

Conclusion

Chapter 3: Education and . . . Big Data versus Big-But-Buried Data

Data , Information, and Knowledge

Big Data Simpliciter (BD) Versusus Big-But -Buried Data (B3D)

Two Anticipated Questions, Two Answers

The Example of Calculus

The Paradox of the Arrow

The Future

Next Steps

Part II: Access, Completion, Success

Chapter 4: Big Data's Impact on College Admission Practices and Recruitment Strategies

Introduction

Defining Big Data in the College Admissions and Enrollment Management Context: Extreme Information Management to Improve Student Access and Success

Ethics and Standards are a First Priority of any Student Data Endeavor

Strong Enrollment Management has Always Been Data Driven

Sem: Recruitment and Retention are two Sides of the Same Coin

Small Data Exposing Student Choice Factors: Curent Enrollment Trends Emerging from Deep Data Sharing and Analytics.

Curent Trends in Traditional Student Enrollments and College Choice

Big Data's Potential to more Clearly Segment Student Populations and Identify Early Needs

Big Data's Potential to Build Stronger Relationships and Improve Student-Institution Match

Using Big Data to Discover "Why": An Eharmony Model to Improve Student Recruitment?

Using Efm to Embrace Big Data in Admissions and Enrollment Management

Efm Approach to Improve Admissions and Enrollment Management Services?

Getting Started: Preparing to Use Big Data in Admissions and Enrollment Management

Chapter 5: Who is the Big Data Student?

It's Big Data Time for Education

Finding One Student Among Thousands: A Heideggerian Perspective

Understanding the Student as Dasein

Big Data in Education

What is so Different About Big Data?

Big Human Data

Taking the Analysis of Big Data from the Ontic to the Ontological: Finding Dasein Among Das Man

Big Data Implications for the Design of Education

Conclusion: Giving Students the Posibility of an Authentic Course

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

Cooling "Summer Melt"

Choosing the Right Courses

Getting the Academic Green Light

Coping in Class and Life

Counseling by Phone

Building Policies for Success

Chapter 7: Unanticipated Data-Driven Innovation in Higher Education Systems: From Student Success to Course Equivalencies

Strategic Policies and Administrative Capacity: The Relationship Between Policy and Data

Analytic Method and Data

Degreeworks: From Student Success to Course Equivalencies

Phase I: Student-Driven Strategy

Phase II: Data-Driven Innovation

Phase III: System Integration

Discussion

Conclusion.

Appendix 1: Rosary of Suny Strategic Policies and Administrative Capacities

Strategic Policies (in chronological order)

Administrative Capacities (in alphabetical order)

Part III: Policy Development and Institutional Decision Making

Chapter 8: Integrating Data Analytics in Higher Education Organizations: Improving Organizational and Student Success

The Need for a Data-Driven Culture

Data Analytics as an Organizational Innovation

Adopting Data Analytics in Colleges and Universities

Factors That Influence an Academic Leader's Choice of Adoption and Use of Data Analytics

Organizational and Individual Use of Data Analytics

A Model of Individual Adoption of Data Analytics

Chapter 9: The Opportunities, Challenges, and Strategies Associated with the Use of Operations-Oriented (Big) Data to Support Decision Making within Universities

Operational Goals, Research Goals, and Institutional Data

The Accuracy of Operations-Oriented Data

Principles and Strategies for Examining Operations-Oriented Data

Past Research into Instructional Productivity and Costs

Discussion and Conclusion

Chapter 10: Measuring the Internationalization of Higher Education: Data, Big Data, and Analytics

Why Measure Internationalization?

Measuring International Aspects of Higher Education: Sources and Methods

Cautionary Considerations

What Does the Future Hold?

Chapter 11: Big Data and Human Capital Development and Mobility

Demand for Linked Longitudinal Data

Borders can Hinder Data Flow

The Wiche Multistate Data Exchange Pilot

Considerations in Making Appropriate and Effective Use of Linked Longitudinal Data

References.

Contributors

Index.

Notes:

Bibliographic Level Mode of Issuance: Monograph

Includes bibliographical references at the end of each chapters and index.

Description based on print version record.

ISBN:

9781438454542

1438454546

OCLC:

892843228