Learning Analytics Enhanced Online Learning Support

Offering the latest developments in online education in the era of big data, this book explores theories, technologies, and practices in the field of data-driven online learning support services using learning analytics.

This book is divided into five chapters. Chapter 1 reflects and reconstructs the connotation of learning support against the backdrop of education reform, the rise of learning analytics, and the upgrading of the demand for learning services in the new era. Chapter 2 presents a P-K-DSE-E model of online learner characteristics and discusses measurement and data representation methods for learner characteristics based on it. Chapters 3–5 focus on the three types of learning support that are closely related to learning performance and satisfaction, including the promotion of social learning, electronic learning assessment based on the learning process, and personalized tutoring and support. This book innovatively develops the concept, theory, and practical methods of student support services in distance education traditional practices in the new era and provides valuable exploration of data-driven personalized learning service methods and technologies in the era of artificial intelligence through rich examples.

This book will be essential reading for students and scholars of distance and online education, educational technology, and audiovisual education.

Shuang Li is an Associate Professor of the Faculty of Education at Beijing Normal University, China. Her areas of research interest primarily include online learning support services and learning analytics. She is the primary drafter of the national occupational standard for "online learning service professionals" in China. She has published over 100 research papers in core journals and conferences in the field of education, including CE, IJETHE, ILE, and JCAL.