Learning analytics in higher education : current innovations, future potential, and practical applications
Author(s)
Bibliographic Information
Learning analytics in higher education : current innovations, future potential, and practical applications
Routledge, 2019
- : pbk
Available at 3 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Other editors: Carrie Klein, Aditya Johri, Huzefa Rangwala
Includes bibliographical references and index
Description and Table of Contents
Description
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.
Table of Contents
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 usersDavid 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
by "Nielsen BookData"