Matthew L. Bernacki – författare

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.Chapter 13 and Chapter 25 of this book are freely available as a downloadable Open Access PDF at http://www.taylorfrancis.com under a Creative Commons Attribution-Non Commercial-No Derivatives (CC BY-NC-ND) 4.0 license.

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.Chapter 13 and Chapter 25 of this book are freely available as a downloadable Open Access PDF at http://www.taylorfrancis.com under a Creative Commons Attribution-Non Commercial-No Derivatives (CC BY-NC-ND) 4.0 license.