Data Analytics and Adaptive Learning offers new insights into the use of emerging data analysis and adaptive techniques in multiple learning settings. In recent years, both analytics and adaptive learning have helped educators become more responsive to learners in virtual, blended, and personalized environments. This set of rich, illuminating, international studies spans quantitative, qualitative, and mixed-methods research in higher education, K–12, and adult/continuing education contexts. By exploring the issues of definition and pedagogical practice that permeate teaching and learning and concluding with recommendations for the future research and practice necessary to support educators at all levels, this book will prepare researchers, developers, and graduate students of instructional technology to produce evidence for the benefits and challenges of data-driven learning.
Author(s): Patsy D. Moskal, Charles D. Dziuban, Anthony G. Picciano
Publisher: Routledge
Year: 2023
Language: English
Pages: 362
City: New York
Cover
Endorsement Page
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
About the Editors
Preface
Acknowledgements
List of Contributors
Section I: Introduction
Chapter 1 Data analytics and adaptive learning: Increasing the odds
Section II: Analytics
Chapter 2 What we want versus what we have: Transforming teacher performance analytics to personalize professional development
Chapter 3 System-wide momentum
Chapter 4 A precise and consistent early warning system for identifying at-risk students
Chapter 5 Predictive analytics, artificial intelligence and the impact of delivering personalized supports to students from underserved backgrounds
Chapter 6 Predicting student success with self-regulated behaviors: A seven-year data analytics study on a Hong Kong University English Course
Chapter 7 Back to bloom: Why theory matters in closing the achievement gap
Chapter 8 The metaphors we learn by: Toward a philosophy of learning analytics
Section III: Adaptive Learning
Chapter 9 A cross-institutional survey of the instructor use of data analytics in adaptive courses
Chapter 10 Data analytics in adaptive learning for equitable outcomes
Chapter 11 Banking on adaptive questions to nudge student responsibility for learning in general chemistry
Chapter 12 Three-year experience with adaptive learning: Faculty and student perspectives
Chapter 13 Analyzing question items with limited data
Chapter 14 When adaptivity and universal design for learning are not enough: Bayesian network recommendations for tutoring
Section IV: Organizational Transformation
Chapter 15 Sprint to 2027: Corporate analytics in the digital age
Chapter 16 Academic digital transformation: Focused on data, equity, and learning science
Section V: Closing
Chapter 17 Future technological trends and research
Index
