Innovative Learning Analytics for Evaluating Instruction covers the application of a forward-thinking research methodology that uses big data to evaluate the effectiveness of online instruction. Analysis of Patterns in Time (APT) is a practical analytic approach that finds meaningful patterns in massive data sets, capturing temporal maps of students’ learning journeys by combining qualitative and quantitative methods. Offering conceptual and research overviews, design principles, historical examples, and more, this book demonstrates how APT can yield strong, easily generalizable empirical evidence through big data; help students succeed in their learning journeys; and document the extraordinary effectiveness of First Principles of Instruction. It is an ideal resource for faculty and professionals in instructional design, learning engineering, online learning, program evaluation, and research methods.

As we navigate post-pandemic educational recovery and future-oriented design, the Handbook of Research in Online Learning: Insights and Advances emerges as a scholarly authority to illuminate existing questions and catalyze conversations on imperative transformations in education. Tailored for researchers, designers, educators, administrators, and stakeholders, this handbook delves into the nuanced landscape of online learning. Curated by leading experts, each chapter provides a deep exploration of critical online teaching and learning dimensions. Whether you're navigating the complexities of instructional design, exploring the impact of digital learning on diverse student populations, or delving into the transformative potential of AI, each chapter illuminates critical aspects of online education. It merges current significant works with unpublished manuscripts, embodying the interdisciplinary essence of online learning research. Grounded in diverse theoretical frameworks and research methods, it offers theoretical insights and actionable guidance for cutting-edge educational methodologies. This handbook is not just a compendium; it's an indispensable guide for shaping the future of education. Contributors are: Michael Ahlf, Stephen Allen, Tonya Amankwatia, Fatih Ari, Ismahan Arslan-Ari, Michael K. Barbour, Gail Alleyne Bayne, Karen Bellnier, M. Aaron Bond, Victoria Brown, George Bradford, William Cain, Sumie Chan, Lauren Cifuentes, Laura DaVinci, Gina Deckard, Shernette Dunn, Anne Fensie, Holly Fiock, Sara Flowers, Carla Karen Fortune, Theodore Frick, Michael M. Grant, Alexis Guethler, Dan He, Atsusi "2c" Hirumi, Charles B. Hodges, Stephanie Hostetter, Michael Houdyshell, Fethi A. Inan, Frank Jamison, Amir Kalan, Meryl Krieger, Jessica Lantz, Mary Lefaiver, Juhong Christie Liu, Noble Lo, Barbara Lockee, Fatemeh Marzban, Trey Martindale, Sara McNeil, Laura McNeill, Stephanie Moore, Martha Lorena Obermeier, Larisa Olesova, Jennifer Jihae Park, Sanghoon Park, Yujin Park, AnthTony Pina, Drew Polly, Yingxiao Qian, Thomas Reeves, Christiane Reilly, Jennifer Richardson, Aubrey Rogowski, Leanne Rutherford, Kay Seo, Sanga Song, Edwin Teye Sosi, Stefan Stenbom, Sharon Stidham, David Tai, Hengtao Tang, Torrey Trust, Shannon Tucker, Denis Unal, Lucas Vasconcelos, Charles Xiaoxue Wang, Florence Williams, Ying Xie and Fan Xu.

Students often enter higher education academically unprepared and with unrealistic perceptions and expectations of university life, which are critical factors that influence students’ decisions to leave their institutions prior to degree completion. Advances in educational technology and the current availability of vast amounts of educational data make it possible to represent how students interact with higher education resources, as well as provide insights into students’ learning behavior and processes. This volume offers new research in such learning analytics and demonstrates how they support students at institutions of higher education by offering personalized and adaptive support of their learning journey. It focuses on four major areas of discussion: · Theoretical perspectives linking learning analytics and study success. · Technological innovations for supporting student learning. · Issues and challenges for implementing learning analytics at higher education institutions. · Case studies showcasing successfully implemented learning analytics strategies at higher education institutions. Utilizing Learning Analytics to Support Study Success ably exemplifies how educational data and innovative digital technologies contribute to successful learning and teaching scenarios and provides critical insight to researchers, graduate students, teachers, and administrators in the general areas of education, educational psychology, academic and organizational development, and instructional technology.

Augmented reality (AR) and virtual reality (VR) provide flexibility in education and have become widely used for the promotion of multimedia learning. This use coincides with mobile devices becoming prevalent, VR devices becoming more affordable, and the creation of user-friendly software that allows the development of AR/VR applications by non-experts. However, because the integration of AR and VR into education is a fairly new practice that is only in its initial stage, these processes and outcomes need to be improved. Designing, Deploying, and Evaluating Virtual and Augmented Reality in Education is an essential research book that presents current practices and procedures from different technology-implementation stages (design, deployment, and evaluation) to help educators use AR/VR applications in their own teaching practices. The book provides comprehensive information on AR and VR applications in different educational settings from various perspectives including but not limited to mobile learning, formal/informal learning, and integration strategies with practical and/or theoretical implications. Barriers and challenges to their implementation that are currently faced by educators are also addressed. This book is ideal for academicians, instructors, curriculum designers, policymakers, instructional designers, researchers, education professionals, practitioners, and students.

The field of education is in constant flux as new theories and practices emerge to engage students and improve the learning experience. Research advances help to make these improvements happen and are essential to the continued improvement of education. The Handbook of Research on Applied Learning Theory and Design in Modern Education provides international perspectives from education professors and researchers, cyberneticists, psychologists, and instructional designers on the processes and mechanisms of the global learning environment. Highlighting a compendium of trends, strategies, methodologies, technologies, and models of applied learning theory and design, this publication is well-suited to meet the research and practical needs of academics, researchers, teachers, and graduate students as well as curriculum and instructional design professionals.

This book is a collection of selected research papers presented at the 2023 4th International Conference on Artificial Intelligence in Education Technology (AIET 2023), held in Berlin, Germany, on June 30 - July 2, 2023. AIET establishes a platform for AI in education researchers to present research, exchange innovative ideas, propose new models, as well as demonstrate advanced methodologies and novel systems. It is a timely and up-to-date publication responsive to the rapid development of AI technologies, practices and their increasingly complex interplay with the education domain. It promotes the cross-fertilisation of knowledge and ideas from researchers in various fields to construct the interdisciplinary research area of AI in Education. These subject areas include computer science, cognitive science, education, learning sciences, educational technology, psychology, philosophy, sociology, anthropology and linguistics. The feature of this book will contribute from diverse perspectives to form a dynamic picture of AI in Education. It also includes various domain-specific areas for which AI and other education technology systems have been designed or used in an attempt to address challenges and transform educational practice. This timely publication is in line with UNESCO’s Beijing Consensus on Artificial Intelligence and Education. It is committed to exploring how AI may play a role in bringing more innovative practices, transforming education, and triggering an exponential leap towards the achievement of the Education 2030 Agenda. Providing broad coverage of recent technology-driven advances and addressing a number of learning-centric themes, the book is an informative and useful resource for researchers, practitioners, education leaders and policy-makers who are involved or interested in AI and education.

Modern technology is creating new challenges for teachers to keep up with today’s education and advancing teaching styles. The development of a differentiated curriculum is necessary and needed particularly for a diversified group of students. Differentiated Instructional Design for Multicultural Environments: Emerging Research and Opportunities provides emerging research on modern technology advancements and their uses in education. While highlighting new trends in teaching a diverse group of students, readers will discover the need for a differentiated approach to curriculum development for multicultural learners. This book is a vital resource for academics, teachers, preservice teachers, and researchers seeking current research on how technological advancements, teachers, and multicultural student groups blend in today’s educational atmosphere.