Blended Learning Environments to Foster Self-directed Learning - Roxanne Bailey (https://orcid.org/0000-0001-5326-274X); Mncedisi C. Maphalala (https://orcid.org/0000-0002-1078-1985); Christo van der Westhuizen (https://orcid.org/0000-0002-4762-8538); Nicole Bergamin (https://orcid.org/0000-0002-0638-6325); Per B. Bergamin (https://orcid.org/0000-0002-2551-9058); Betty Breed (https://orcid.org/0000-0002-1127-4985); Byron J. Bunt (https://orcid.org/0000-0002-2102-4381); Joyce P. Dhlamini (https://orcid.org/0000-0002-6903-7843); Divan Jagals (https://orcid.org/0000-0001-5840-6298); Albert Kemp (https://orcid.org/0000-0002-5335-262X); Corné Kruger (http://orcid.org/0000-0003-4042-4476); Donnavan Kruger (https://orcid.org/0000-0003-2013-261X); et al. (AOSIS Books, 2022)
This book on blended learning environments to foster self-directed learning highlights the focus on research conducted in several teaching and learning contexts where blended learning had been implemented and focused on the fostering of self-directed learning. Several authors have contributed to the book, and each chapter provides a unique perspective on blended learning and self-directed learning research. From each chapter, it becomes evident that coherence on the topics mentioned is established. One of the main aspects drawn in this book, and addressed by several authors in the book, is the use of the Community of Inquiry (CoI) framework when implementing teaching and learning strategies in blended learning environments to foster self-directed learning. This notion of focusing on the CoI framework is particularly evident in both theoretical and empirical dissemination presented in this book. What makes this book unique is the fact that researchers and peers in varied fields would benefit from the findings presented by each chapter, albeit theoretical, methodological or empirical in nature – this, in turn, provides opportunities for future research endeavours to further the narrative of how blended learning environments can be used to foster self-directed learning