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E-raamat: Data Analytics and Adaptive Learning: Research Perspectives [Taylor & Francis e-raamat]

Edited by (University of Central Florida, USA), Edited by , Edited by (University of Central Florida, USA)
  • Formaat: 334 pages, 42 Tables, black and white; 44 Line drawings, black and white; 10 Halftones, black and white; 54 Illustrations, black and white
  • Ilmumisaeg: 25-Aug-2023
  • Kirjastus: Routledge
  • ISBN-13: 9781003244271
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  • Taylor & Francis e-raamat
  • Hind: 161,57 €*
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  • Tavahind: 230,81 €
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Data Analytics and Adaptive Learning: Research PerspectivesSuurem pilt
  • Formaat: 334 pages, 42 Tables, black and white; 44 Line drawings, black and white; 10 Halftones, black and white; 54 Illustrations, black and white
  • Ilmumisaeg: 25-Aug-2023
  • Kirjastus: Routledge
  • ISBN-13: 9781003244271
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9781003244271
"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 exploringthe 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"--

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.



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

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
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.
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