Learning Analytics in Higher Education

Current Innovations, Future Potential, and Practical Applications
 
 
Routledge (Verlag)
  • 1. Auflage
  • |
  • erscheint ca. am 13. August 2018
 
  • Buch
  • |
  • Softcover
  • |
  • 224 Seiten
978-1-138-30217-4 (ISBN)
 
Learning Analytics in Higher Education provides a foundational understanding of how learning analytics is defined, what barriers and opportunities exist, and how it can be used to improve practice, including strategic planning, course development, teaching pedagogy, and student assessment. Well-known contributors provide empirical, theoretical, and practical perspectives on the current use and future potential of learning analytics for student learning and data-driven decision making; ways to effectively evaluate and research learning analytics; integration of learning analytics into practice; organizational barriers and opportunities for harnessing big data to create and support use of these tools; and ethical considerations related to privacy and consent. Designed to give readers a practical and theoretical foundation in learning analytics and how data can support student success in higher education, this book is a valuable resource for scholars and administrators.
  • Englisch
  • London
  • |
  • Großbritannien
Taylor & Francis Ltd
  • Für höhere Schule und Studium
  • 18
  • |
  • 9 s/w Tabellen, 18 s/w Abbildungen
  • |
  • 9 Tables, black and white; 16 Illustrations, black and white
  • Höhe: 229 mm
  • |
  • Breite: 152 mm
978-1-138-30217-4 (9781138302174)
1138302171 (1138302171)
weitere Ausgaben werden ermittelt
Jaime Lester is currently an Assistant Professor of Higher Education, George Mason University, USA.

Huzefa Rangwala is an Associate Professsor of Computer Science at George Mason University, USA.

Aditya Johri is an Associate Professor in the Department of Information Sciences and Technology at George Mason University,USA.

Carrie Klein is a Ph.D. student in the Higher Education Program at George Mason University, USA.
Contents

List of Tables

List of Figures

Preface

Acknowledgments

Chapter 1: Absorptive capacity and routines: Understanding barriers to learning analytics adoption in higher education
Aditya Johri

Chapter 2. Analytics in the field: Why locally grown continuous improvement systems are essential for effective data driven decision-making
Matthew T. Hora

Chapter 3: Big data, small data, and data shepherds
Jennifer DeBoer and Lori Breslow

Chapter 4: Evaluating scholarly teaching: A model and call for an evidence-based approach
Daniel L. Reinholz, Joel C. Corbo, Daniel J. Bernstein, and Noah D. Finkelstein

Chapter 5: Discipline-focused learning analytics approaches with users instead of for users
David B. Knight, Cory Brozina, Timothy J. Kinoshita, Brian J. Novoselich, Glenda D. Young, and Jacob R. Grohs

Chapter 6: Student consent in learning analytics: The devil in the details?
Paul Prinsloo and Sharon Slade

Chapter 7: Using learning analytics to improve student learning outcomes assessment in higher education: Potential, constraint, & possibility

Carrie Klein, and Richard M. Hess

Chapter 8: Data, data everywhere: Implications and considerations

Matthew D. Pistilli

Contributor Bios

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