Winner of two Outstanding Book Awards from the Association of Educational Communications and Technology (Culture, Learning, & Technology and Systems Thinking & Change divisions)! Rapid advancements in our ability to collect, process, and analyze massive amounts of data along with the widespread use of online and blended learning platforms have enabled educators at all levels to gain new insights into how people learn. Responsible Analytics and Data Mining in Education addresses the thoughtful and purposeful navigation, evaluation, and implementation of these emerging forms of educational data analysis. Chapter authors from around the world explore how data analytics can be used to improve course and program quality; how the data and its interpretations may inadvertently impact students, faculty, and institutions; the quality and reliability of data, as well as the accuracy of data-based decisions; ethical implications surrounding the collection, distribution, and use of student-generated data; and more. This volume unpacks and explores this complex issue through a systematic framework whose dimensions address the issues that must be considered before implementation of a new initiative or program.

Higher education has seen an increase in attention to social change and social responsibility. Providing best practices in these areas will help professionals to create methods for change and suggestions for unity on a global level. Examining Social Change and Social Responsibility in Higher Education is an essential research publication that explores current cultural norms and their influence on curriculum and educational environments and intends to improve the understanding of social change and social responsibility at different sociological levels within various fields pertaining to higher education. Highlighting topics such as campus safety, social justice, and mental health, this book is ideal for academicians, professionals, researchers, administrators, and students working in various disciplines (e.g., academic advising, leadership, higher education, adult education, campus climate, Title IX, SAVE/VAWA, and more). Moreover, the book will provide insights and support executives concerned with the management of expertise, knowledge, information, and organizational development in different types of work communities and environments.

The purpose of AECT at 100: A Legacy of Leadership is to highlight the Association for Educational Communications and Technology’s 100 years of leadership in educational technology and learning. AECT has a rich history, evolving from the National Education Association’s (NEA) Department of Visual Instruction (DVI) and later the Department of Audio-Visual Instruction (DAVI). Over its 100 years, AECT and its members have had a substantial impact on the evolution of American educational technology and learning, including in the areas of audiovisual instruction, instructional design, and online learning. AECT at 100: A Legacy of Leadership brings together writers and experts in the organization to explore various periods of history within the field and how AECT and its membership stood as a leader within the field. Topics such as visual instruction, the audiovisual movement, leadership development, programmed instruction, diversity leadership, AECT and educational technology topics, journals, ethics, and social justice are explored. Additionally, a number of leaders are explored from the early days of AECT such as James Finn, F. Dean McClusky, Edgar Dale, and Elizabeth Golterman all the way to recent leaders such as Rob Branch.

Beyond their educational value, university institutions can play a pivotal role in community improvement. By utilizing academic resources, these organizations can positively impact their communities. Engaged Scholarship and Civic Responsibility in Higher Education is a critical reference source for the latest scholarly research on the adoption and implementation of civic engagement initiatives in higher education institutions. Including a range of pertinent topics such as service learning, economic development, and social justice, this book is ideally designed for academics, practitioners, students, professionals, and researchers interested in the growing influence of universities on community improvement.

In today’s world of global change, it has never been more critical for businesses to adapt their strategies, management styles, and policies to match that of the surrounding environment. Consumers are increasingly becoming more aware of the impact that businesses and product consumption have on environmental health. Thus, businesses have had to alter their production workflows to better suit a more environmentally conscious customer base. As society faces numerous issues related to the environment, health, poverty, and social justice, the need for socially responsible businesses is crucial to develop and improve the overall landscape of the business field. The Research Anthology on Developing Socially Responsible Businesses discusses the best practices, challenges, opportunities, and benefits of creating socially responsible businesses and provides a context of why these business models are needed. This essential text also considers how society has changed over time and how businesses must adjust their ideals and practices in order to survive in a changing world. Covering a range of topics such as accountability, environmental issues, and human rights, this major reference work is ideal for business owners, managers, policymakers, academicians, researchers, scholars, practitioners, instructors, and students.

MACHINE LEARNING FOR BUSINESS ANALYTICS Machine learning—also known as data mining or predictive analytics—is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques, and Applications with Analytic Solver® Data Mining provides a comprehensive introduction and an overview of this methodology. The fourth edition of this best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, time series forecasting and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This fourth edition of Machine Learning for Business Analytics also includes: An expanded chapter on deep learning A new chapter on experimental feedback techniques, including A/B testing, uplift modeling, and reinforcement learning A new chapter on responsible data science Updates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their students A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.

The book aims to advance global knowledge and practice in applying data science to transform higher education learning and teaching to improve personalization, access and effectiveness of education for all. Currently, higher education institutions and involved stakeholders can derive multiple benefits from educational data mining and learning analytics by using different data analytics strategies to produce summative, real-time, and predictive or prescriptive insights and recommendations. Educational data mining refers to the process of extracting useful information out of a large collection of complex educational datasets while learning analytics emphasizes insights and responses to real-time learning processes based on educational information from digital learning environments, administrative systems, and social platforms. This volume provides insight into the emerging paradigms, frameworks, methods and processes of managing change to better facilitate organizational transformation toward implementation of educational data mining and learning analytics. It features current research exploring the (a) theoretical foundation and empirical evidence of the adoption of learning analytics, (b) technological infrastructure and staff capabilities required, as well as (c) case studies that describe current practices and experiences in the use of data analytics in higher education.

Trust is a pervasive catalyst of human and business relationships that has inspired interest in researchers and practitioners alike. It has been shown to enhance engagement, communication, organizational performance, and online activities. Despite its role to cultivate cooperation, knowledge-sharing, and innovation, trust through digital means or even trust in digital media has presented new opportunities and challenges in society. Examples include a wider and faster dissemination of trust-influencing messages, and richer options of digital cues that engage, disrupt, or even transform how trust is formulated. Despite that, trust helps people to live through risky and uncertain situations, and the many capabilities enabled on the digital platforms have made the formation and sustaining of trust very different compared to traditional means. Trust in today’s digital environment plays an important role and is intertwined with concepts including reliability, quality, and privacy. This book aims to bring together the theory and practice of trust in the new digital era and will present theoretical and practical foundations. Trust is not given; we must work to build it, but it is a very fragile and intangible asset once built. It is easy to destroy and challenging to rebuild. Researchers, academics, and students in the fields of management, responsibility, and business ethics will gain knowledge on trust and related concepts, learn about the theoretical underpinnings of trust and how it sustains itself through digital dissemination, and explore empirically validated practice regarding trust and its related concepts.

This book is about the process of using analytics and the capabilities of analytics in today’s organizations. Cutting through the buzz surrounding the term analytics and the overloaded expectations about using analytics, the book demystifies analytics with an in-depth examination of concepts grounded in operations research and management science. Analytics as a set of tools and processes is only as effective as: The data with which it is working The human judgment applying the processes and understanding the output of these processes. For this reason, the book focuses on the analytics process. What is intrinsic to analytics’ real organizational impact are the careful application of tools and the thoughtful application of their outcomes. This work emphasizes analytics as part of a process that supports decision-making within organizations. It wants to debunk overblown expectations that somehow analytics outputs or analytics as applied to other concepts, such as Big Data, are the be-all and end-all of the analytics process. They are, instead, only a step within a holistic and critical approach to management thinking that can create real value for an organization. To develop this holistic approach, the book is divided into two sections that examine concepts and applications. The first section makes the case for executive management taking a holistic approach to analytics. It draws on rich research in operations and management science that form the context in which analytics tools are to be applied. There is a strong emphasis on knowledge management concepts and techniques, as well as risk management concepts and techniques. The second section focuses on both the use of the analytics process and organizational issues that are required to make the analytics process relevant and impactful.

This book demonstrates teachers’ and learners’ experiences with big data in education; education and cloud computing; and new technologies for teacher support. It also discusses the advantages of using these frontier technologies in teaching and learning and predicts the future challenges. As such, it enables readers to better understand how technologies can improve learning and teaching experiences. It is intended for graduates and scholars in educational technology disciplines and anyone interested in the applications of frontier technologies in education.