Data Analytics and Adaptive Learning (eBook, PDF)

Research Perspectives
Redaktion: Moskal, Patsy D.; Picciano, Anthony G.; Dziuban, Charles D.

• Format: PDF

Produktbeschreibung

Data Analytics and Adaptive Learning offers new insights into the use of emerging data analysis and adaptive techniques in multiple learning settings.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 364
• Erscheinungstermin: 25. August 2023
• Englisch
• ISBN-13: 9781000929232
• Artikelnr.: 68339919

Autorenporträt

Patsy D. Moskal is Director of the Digital Learning Impact Evaluation in the Research Initiative for Teaching Effectiveness at the University of Central Florida, USA. Charles D. Dziuban is Director of the Research Initiative for Teaching Effectiveness at the University of Central Florida, USA. Anthony G. Picciano is Professor of Education Leadership at Hunter College and Professor in the PhD program in Urban Education at the City University of New York Graduate Center, USA.

Inhaltsangabe

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 -
Tony Picciano