Online learning has increasingly been viewed as a possible way to remove barriers associated with traditional face-to-face teaching, such as overcrowded classrooms and shortage of certified teachers. While online learning has been recognized as a possible approach to deliver more desirable learning outcomes, close to half of online students drop out as a result of student-related, course-related, and out-of-school-related factors (e.g., poor self-regulation; ineffective teacher-student, student-student, and platform-student interactions; low household income). Many educators have expressed concern over students who unexpectedly begin to struggle and appear to fall off track without apparent reason. A well-implemented early warning system, therefore, can help educators identify students at risk of dropping out and assign and monitor interventions to keep them on track for graduation. Despite the popularity of early warning systems, research on their design and implementation is sparse. Early Warning Systems and Targeted Interventions for Student Success in Online Courses is a cutting-edge research publication that examines current theoretical frameworks, research projects, and empirical studies related to the design, implementation, and evaluation of early warning systems and targeted interventions and discusses their implications for policy and practice. Moreover, this book will review common challenges of early warning systems and dashboard design and will explore design principles and data visualization tools to make data more understandable and, therefore, more actionable. Highlighting a range of topics such as curriculum design, game-based learning, and learning support, it is ideal for academicians, policymakers, administrators, researchers, education professionals, instructional designers, data analysts, and students.

"With this in mind, this edited volume will examine current theoretical frameworks, research projects and empirical studies related to the design, implementation and evaluation of self-regulated learning models and SRL interventions in online courses, and discuss their implications for researchers, policymakers, instructional designers, and practitioners. This volume seeks to shed light on new instructional models which support self-regulated learning, and afford new ways of designing online instruction to promote self-regulation, including how to develop SRL interventions that improve student success, retention and engagement in online courses"--

This book is a valuable resource for graduate students in student affairs preparation programs and anyone working in the field of student affairs. It provides readers with a comprehensive guide to the integration of technology in student affairs, drawing on the expertise of leading scholars and practitioners in the field. The chapters in this book explore the many ways in which technology can be used to enhance student engagement, support, and learning outcomes. The topics include the history of technology in student affairs, digital identity, integrating high-tech with high touch on campus, ethical issues with technology, technology and risk management, consideration of the need for social media liaisons in student affairs, technology competency and adaptability, and case study on technology decision-making. Whether you are a seasoned professional or a new practitioner, you can gain a wealth of insights on how to use theoretical models to address digital identity, convert traditional campus functions to technological approaches, apply a conceptual framework for ethical technological practices, enhance campus and community engagement through the development of a social media liaison, navigate technological competency and adaptability issues on campus as well as to identify legal issues arising in daily practice and learn risk management skills. The knowledge, skills, and wisdom provided in this book will guide you not only to incorporate technology into your work but also empower you to leverage technology in innovative ways to better serve the needs of your students and advance the field of student affairs.

Infrastructure Possibilities and Human-Centered Approaches With Industry 5.0 is a research book that serves as a comprehensive exploration of the potential impact of Industry 5.0 and the research opportunities presented by it, a new era of industrial revolution that integrates advanced technologies with human expertise and creativity. This book delves into the transformative effects of Industry 5.0 on society, with a particular focus on human-centric approaches and the key areas of agriculture, transportation, healthcare, and more. The book examines the revolutionary impact of Industry 5.0 in various domains. It explores the application of AI and machine learning in revolutionizing agriculture, improving livestock management, optimizing fertilizer usage, and detecting agricultural diseases. Additionally, it delves into the integration of advanced technologies in healthcare, including wearable devices, sensors, and robotics, to provide personalized and efficient healthcare services. Furthermore, the book explores the implications of Industry 5.0 on transportation, smart grid systems, and education. Throughout the discussion, the book addresses the ethical and social considerations associated with Industry 5.0, such as privacy, data protection, and social inequality. Written for research scholars, graduate engineering students, and postgraduate students in the fields of computer science, agriculture, and health engineering, this book serves as a valuable resource for understanding the transformative potential of Industry 5.0.

As classrooms move from chalkboards to digital platforms, there's a critical juncture where the potential of AI intersects with the future of academia. The problem is multifaceted — it involves the nature of pedagogy, the demand for personalized learning experiences, and the ethical considerations inherent in deploying AI technologies. Educators, researchers, and administrators face the challenge of navigating this transformation while ensuring inclusivity, fairness, and ethical practice. Integrating AI in higher education risks becoming a chaotic endeavor without a comprehensive understanding of the opportunities, challenges, and ethical dimensions. The lack of a strategic approach could lead to biases, privacy concerns, and a digital divide exacerbating educational inequalities. Enhancing Higher Education and Research With OpenAI Models explores the intersection of artificial intelligence and higher education, focusing on the social sciences. A collaborative team of academics and AI expert's analyses aims to illuminate the transformative potential of integrating AI technologies into traditional educational settings. The book unravels the rich tapestry of the history of higher education in the social sciences, tracing the evolution from conventional blackboards to the modern digital landscape. It meticulously examines the increasing integration of technology in classrooms. It sets the stage for the impact of AI-driven tools and data analytics on pedagogy, personalized learning experiences, and broader access to education.

"Big Data in Education" explores the revolutionary impact of big data on the educational landscape. In this comprehensive guide, educators, administrators, policymakers, and researchers will discover how data-driven insights can enhance learning experiences, improve student outcomes, and streamline educational practices. The book begins by defining big data and its characteristics, such as volume, velocity, variety, veracity, and value. It delves into the various sources of educational data, including student information systems, learning management systems (LMS), social media, and IoT devices, as well as the methods for collecting this data.

The purpose of the study was to develop a working model to predict at risk students in an Introduction to Engineering course. The model considers both students' pre-college characteristics, psychological traits, and online homework learning behavior. The study assisted the course instructor in the creation of an early warning system and the development of targeted interventions for students at risk. A reliable and valid instrument to measure engineering students' pre-college characteristics was initially developed. The study also applied data mining to analyze the student online homework logs in order to observe engineering students' homework learning process. A decision tree model containing all of the pre-college characteristics and online homework learning features was also developed, and it identified four key factors related to students' risk to fail the first module exam: Correctness, Preparedness, Self-efficacy, and percentage of homework attempts after deadline (Plate). The results of the decision tree model helped identify students-at-risk at early stage of the course. Students at risk were grouped into multiple groups. The author also proposed customized interventions to help students in different at risk groups. The findings of the study helped engineering students and educators to build up a comprehensive student profile to better understand students' academic status and learning needs in the course. Thus this study suggests ways for both the engineering educators and students to improve the learning process in a more efficient manner.