New Book on “Data Analytics and Adaptive Learning” by Patsy Moskal, Charles Dziuban and Anthony Picciano!

Dear Commons Community,

I am pleased to announce that a new book entitled, Data Analytics and Adaptive Learning:  Research Perspectives  edited by Patsy Moskal, Charles Dziuban, and Anthony G. Picciano was just published yesterday and is available at Routledge/Taylor & Francis and at Amazon.com

My co-editors and I provide new insights into the growing use of data analytics in education based on the views of a world-class group of leaders and researchers.  It explores issues of definition and pedagogical practice that permeate teaching and learning and concludes with recommendations for the future research and practice necessary to support educators at all levels.

Here is what some of our reviewers have had to say:

“This book is an essential guide to the promise and practice of data analytics and adaptive learning in higher education. These pioneers and practitioners share valuable insights all institutions can use to enhance learning and student success.”

―Diana G. Oblinger, Ph.D., President Emeritus, EDUCAUSE

“Impacts on learning we called overdetermined can now be parsed but need an informed judgment rising to the complexities involved. Data Analytics and Adaptive Learning, a collection of the very best thinking about both, provides just that, rendering the (potentially) all-seeing and hyper-focused approaches of DA and AL fruitful, humane, transformative.”

―George Otte, former University Director of Academic Technology, The City University of New York

“At last: a book by education experts about the use of digital Information and Communication Technologies (ICTs) not only for lowering the friction in data, but for processing information to help teachers and students. This can be the beginning of a more radical change in education.”

―Anders Norberg, Coordinator of the ERASMUS+ALBATTS Blueprint Project – European Union Initiative for Education Mobility and Development)

“Digital learning is the new normal in higher education. The group of experts assembled in this book share important ideas and trends related to learning analytics and adaptive learning that will surely influence all of our digital learning environments in the future.”

―Charles R. Graham, Professor, Department of Instructional Psychology and Technology, Brigham Young University

“The concept of personalized and adaptive learning has long been touted but seldom enacted in education at scale. Data Analytics and Adaptive Learning brings together a compelling set of experts that provide novel and research informed insights into contemporary education spaces.”

―Professor Shane Dawson, Executive Dean Education Futures, University of South Australia

“Moskal, Dziuban, and Picciano challenge the reader to keep the student at the center and imagine how data analytics and adaptive learning can be mutually reinforcing in closing the gap between students from different demographics.”

―Susan Rundell Singer, Vice President for Academic Affairs and Provost, Rollins College, former Division Director for Undergraduate Education at the National Science Foundation

Below is the Table of Contents.

I hope you consider picking up a copy and let us know what you think!

Tony

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Table of Contents

Section 1: Introduction

1. Data Analytics and Adaptive Learning: Increasing the Odds

Section 2: Analytics

2. What We Want Versus What We Have: Transforming Teacher Performance Analytics to Personalize Professional Development

3. System-Wide Momentum

4. A Precise and Consistent Early Warning System for Identifying At-Risk Students

5. Predictive Analytics, Artificial Intelligence and the Impact of Delivering Personalized Supports to Students from Underserved Backgrounds

6. Predicting Student Success with Self-regulated Behaviors: A Seven-year Data Analytics Study on a Hong Kong University English Course

7. Back to Bloom: Why Theory Matters in Closing the Achievement Gap

8. The Metaphors We Learn By: Toward a Philosophy of Learning Analytics

Section 3: Adaptive Learning

9. A Cross-Institutional Survey of the Instructor Use of Data Analytics in Adaptive Courses

10. Data Analytics in Adaptive Learning for Equitable Outcomes

11. Banking on Adaptive Questions to Nudge Student Responsibility for Learning in General Chemistry

12. 3-Year Experience with Adaptive Learning: Faculty and Student Perspectives

13. Analyzing Question Items with Limited Data 14. When Adaptivity and Universal Design for Learning are Not Enough: Bayesian Network Recommendations for Tutoring

Section 4: Organizational Transformation

15. Sprint to 2027: Corporate Analytics in the Digital Age

16. Academic Digital Transformation: Focused on Data, Equity and Learning Science

Section 5: Closing

17. Future Technological Trends and Research

 

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