Cases On Transnational Learning And Technologically Enabled Environments

"This book offers case studies on divergent themes addressing the core perspecitve of technological adaptability and transnational learning"--Provided by publisher.

Technology is essential for access to learning and development of a knowledge society. Cases on Interactive Technology Environments and Transnational Collaboration: Concerns and Perspectives provides a comparative and comprehensive analysis of technologically enabled educational environments and various issues concerning education and collaborations across the world while also focusing on best practices and experiences from a varied range of countries.

"This book provides a forum for researchers and practitioners to discuss the current and potential impact of online learning and training and to formulate methodologies for the creation of effective learning systems"--Provided by publisher.

After centuries of rethinking education and learning, the current theory is based on technology’s approach to and affect on the planned interaction between knowledge trainers and trainees. Online Tutor 2.0: Methodologies and Case Studies for Successful Learning demonstrates, through the exposure of successful cases in online education and training, the necessity of the human factor, particularly in teaching/tutoring roles, for ensuring the development of quality and excellent learning activities. The didactic patterns derived from these experiences and methodologies will provide a basis for a more powerful and efficient new generation of technology-based learning solutions for high school teachers, university professors, researchers, and students at all levels of education.

The quality of students’ learning experiences is a critical concern for all educational institutions. With the assistance of modern technological advances, educational establishments have the capability to better understand the strengths and weaknesses of their learning programs. Impact of Learning Analytics on Curriculum Design and Student Performance is a critical scholarly resource that examines the connection between learning analytics and evaluations and their impact on curriculum design and student performance in educational institutions. Featuring coverage on a broad range of topics, such as academic support, large scale assessment, and educational research methods, this book is geared towards educators, professionals, school administrators, researchers, and practitioners in the field of education.

The volume provides insights on strategies and technologies for teaching and learning that are being used in unique national/cultural contexts of Africa, Asia and the Middle East, Europe, Latin America, and North America.

Currently, society is becoming increasingly involved in self-directed learning for both intellectual and professional development. As such, it is imperative that both researchers and educational professionals update themselves on the current research on self-directed learning and how it has created a new culture of independence. Emerging Self-Directed Learning Strategies in the Digital Age is a key resource for the latest research on the role of self-directed learning and how it is impacting society, educational professionals, and researchers in their day-to-day work. Featuring broad coverage among a variety of relevant views and themes, such as project based learning (PBL), student goal setting, and self-efficacy theory, this book is ideally designed for educational professionals working in the fields of teacher pedagogy, theory, instructional technology, and adult education, as well as academics and upper-level students looking for innovative research on one-to-one programs and learning analytics.

Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.