Shuang Li – författare

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.

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.

This book systematically investigates the Mars entry problem from the perspectives of deterministic optimization, uncertainty optimization, and guidance. Began with a detailed review of the robotic missions and human-scaled exploration plans to Mars, theories or concepts of optimal control, uncertainty quantification, robust optimization, model predictive control, sequential convex programming, and computational guidance are subsequently introduced. Correspondingly, this book presents a series of trajectory planning and guidance algorithms to improve the robustness, reliability, and safety of the Mars missions. Because the Mars entry problem is studied using advanced mathematics, including probability theory, optimization theory, and cybernetics, thus the book is primarily designed as a textbook for graduate students in aerospace engineering, aeronautics, and astronautics departments. Engineers and researchers may also use this book as a reference or tutorial to help with the modeling and simulation of the Mars entry problem due to its thorough simulations and analyses.