The Handbook of Personalized Learning offers a theoretically grounded conceptualization for the development and implementation of personalizing learning.
The Handbook of Personalized Learning offers a theoretically grounded conceptualization for the development and implementation of personalizing learning. This comprehensive volume addresses personalized learning’s roots in educational, cognitive, and social psychological studies of learning as well as in practice. Positioned to shape the future of personalized learning, this handbook documents past innovations achieved in educational technology research and development; considers how advancements in learning analytics and machine learning have influenced policy and implementation; and showcases current and future applications of personalized learning in diverse K-12 classrooms, higher education, and informal educational settings. The book’s varied, rigorous contributions are informed by an overarching model of personalized learning that centers the assets individuals bring to learning opportunities (including their prior knowledge, interests, self-beliefs, autonomy, and identity) and whose responsive designs build on those assets to improve learning and attainment. Researchers, developers, teaching faculty, and graduate students across educational psychology, educational technology, the learning sciences, learning analytics, human-computer interaction, and beyond will come away with substantive foundations and cutting-edge exemplars of the ways in which designs can be personalized to promote learners’ experiences in educational settings.
Section
1. An Introduction to Personalized Learning
1. Trends in
Personalized Learning
2. A History & Taxonomy of Personalized Learning
3. An
Extensible Model of Personalized Learning Section
2. Exemplary Models of
Personalized and Adaptive Learning
4. An Exemplar: Personalized Learning from
the Perspective of Intelligent Tutoring Systems
5. An Exemplar: Personalized
Learning in Educational Games Using Stealth Assessment Section
3.
Psychological Theories of Learning that Can Inform Personalized Learning
Design
6. Adapting to Prior and Developing Knowledge
7. Personalizing
Learning to Promote Interest
8. Personalizing Learning to Promote Relevance
9. Personalizing with Choices to Promote Autonomy: A Self-Determination
Perspective on Personalized Learning Design
10. Personalizing to Individual
Differences: Promoting Inclusive and Equitable Opportunities for Learning
Section
4. Instructional Designs for Personalized Learning
11. Culturally
Responsive Personalized Learning
12. Leveraging Digital Curation for
Personalized Learning
13. Project-Based Instruction as Personalized Learning
14. Personalized Learning in the Age of Generative Artificial Intelligence:
Opportunities and Challenges Section
5. PL in Academic Subjects
15.
Personalized Learning in Mathematics
16. Personalized Learning in English
Language Arts: Reading Comprehension
17. Personalized Learning in Social
Studies
18. Theoretical, Empirical, and Illustrative Considerations of
Personalized Learning within Computer Science Education Section
6.
Personalization For Learners, Teachers, and Communities
19. Personalized
Learning for Early Learners
20. Applying Self-Determination Theory to the
Effective Implementation of Personalized Learning in Online Higher Education
21. Holistic Individualized Coaching: A Personalized Learning Approach to
Support Teacher Learning
22. Anishinaabe Indigenous & Extensible Models of
(Inter-Species)-Personalized Learning: Towards a Holistic Revision Section
7.
Implementing Personalization: Policy, Technology, and Contextual
Considerations
23. Understanding Personalization and Individualization from
Education Policy Perspectives: Towards an Integrated School-Wide
Implementation System
24. Teacher Perspectives on Implementing Personalized
Learning across Contexts
25. Design and Implementation Factors for Digital
Personalized Learning in Low- and Middle-Income Countries
26. Asset-based
Personalized Learning
Matthew L. Bernacki is Associate Professor of Learning Sciences and Psychological Studies and Kinnard White Endowed Scholar in the School of Education at the University of North Carolina at Chapel Hill, USA.
Candace Walkington is Professor of Mathematics Education and the Annette and Harold Simmons Centennial Chair in the Department of Teaching and Learning at Southern Methodist University, USA.
Alyssa Emery is Assistant Professor of Learning Sciences in the School of Education at Iowa State University, USA.
Ling Zhang is Assistant Professor of Special Education in the College of Education at the University of Wyoming, USA.
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