Data Analytics and Adaptive Learning offers new insights into the use of emerging data analysis and adaptive techniques in multiple learning settings.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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
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
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