This book explores and further expands on the rich history of theoretical and empirical research in open and distributed learning, and addresses the impact of the “data revolution” and the emergence of learning analytics on this increasingly diverse form of educational delivery. Following an introductory chapter that maps the book’s conceptual rationale, the book discusses the potential, challenges and practices of learning analytics in various open and distributed contexts. A concluding chapter briefly summarises the chapters before providing a tentative future research agenda for learning analytics in open and distributed environments.

This book is open access under a CC BY 4.0 license. This book describes the history, structure and institutions of open and distance education in six countries: Australia, Brazil, Canada, Germany, the UK and the US. It discusses how open and distance education is evolving in a digital age to reflect the needs and circumstances of national higher education systems in these countries, and explores the similarities and differences between the ways in which they are organized and structured. It is the first book to make such comparisons and draw conclusions about the nature of open and distance education in the context of various national higher education systems. In a digital era with growing use of online education as well as open and distance education, this book is particularly useful for policy-makers and senior administrators who want to learn about organizing and expanding open and distance education provision. It is also a valuable reference for researchers, academics and students interested in understanding the different approaches to open and distance education.

Learning Analytics Goes to School presents a framework for engaging in education research and improving education practice through the use of newly available data sources and analytical approaches. The application of data-intensive research techniques to understanding and improving learning environments has been growing at a rapid pace. In this book, three leading researchers convey lessons from their own experiences—and the current state of the art in educational data mining and learning analytics more generally—by providing an explicit set of tools and processes for engaging in collaborative data-intensive improvement.

This book explores foundational theories that have been applied in open and distance education (ODE) research and refined to reflect advances in research and practice. In addition, it develops new theories emerging from recent developments in ODE. The book provides a unique and up-to-date source of information for ODE scholars and graduate students, enabling them to make sense of essential theory, research and practice in their field, and to comprehend the gaps in, and need for further enquiry into, theoretical approaches in the digital era. It also offers theory-based advice and guidelines for practitioners, helping them make and justify decisions and actions concerning the development, implementation, research and evaluation of ODE.

Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining’s four guiding principles— prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM’s emerging role in helping to advance educational research—from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields. Includes case studies where data mining techniques have been effectively applied to advance teaching and learning Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research.

This book is the first comprehensive account of developments in open and distance non-formal education in developing countries for over more than 20 years. It includes many instructive and inspiring examples of how international agencies such as UNESCO, FAO, WHO, UNICEF, USAID and the Commonwealth of Learning and national providers are using radio, TV, online and mobile learning, telecentres and other means to achieve the Education for All, Millennium Development and Sustainable Development Goals. It describes the educational needs of the world’s most disadvantaged, vulnerable and least formally educated children, youth and adult populations, including the disabled, refugees and prisoners. It also reports on the successes, outcomes, constraints and shortcomings of using open and distance methods and technology to deliver literacy and numeracy programmes, equivalency, ‘second chance’ or alternative schooling, life skills and rural community development programmes and income generation and vocational training outside the framework of the formal education system. It concludes with suggestions for the extension and improvement of such lifelong learning. Designed to encourage further research and development in these capacity-building practices outside the established formal system, this is a must-read for all policy-makers, managers, educators, students and researchers interested in non-formal education for individuals, families and communities in the developing world.

"In our increasingly digitally enabled education world, analytics used ethically, strategically, and with care holds the potential to help more and more diverse students be more successful on higher education journeys than ever before. Jay Liebowitz and a cadre of the fields best ‘good trouble’ makers in this space help shine a light on the possibilities, potential challenges, and the power of learning together in this work." —Mark David Milliron, Ph.D., Senior Vice President and Executive Dean of the Teachers College, Western Governors University Due to the COVID-19 pandemic and its aftereffects, we have begun to enter the "new normal" of education. Instead of online learning being an "added feature" of K–12 schools and universities worldwide, it will be incorporated as an essential feature in education. There are many questions and concerns from parents, students, teachers, professors, administrators, staff, accrediting bodies, and others regarding the quality of virtual learning and its impact on student learning outcomes. Online Learning Analytics is conceived on trying to answer the questions of those who may be skeptical about online learning. Through better understanding and applying learning analytics, we can assess how successful learning and student/faculty engagement, as examples, can contribute towards producing the educational outcomes needed to advance student learning for future generations. Learning analytics has proven to be successful in many areas, such as the impact of using learning analytics in asynchronous online discussions in higher education. To prepare for a future where online learning plays a major role, this book examines: Data insights for improving curriculum design, teaching practice, and learning Scaling up learning analytics in an evidence-informed way The role of trust in online learning. Online learning faces very real philosophical and operational challenges. This book addresses areas of concern about the future of education and learning. It also energizes the field of learning analytics by presenting research on a range of topics that is broad and recognizes the humanness and depth of educating and learning.