Data Analytics and Adaptive Learning: Research Perspectives

Description

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. 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