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1981- in 2 v.: v.1, Subject index; v.2, Title index, Publisher/title index, Association name index, Acronym index, Key to publishers' and distributors' abbreviations.
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.
This book documents seven examples of Early Warning Systems for hydrometeorological and other hazards that have proven effective in reducing losses due to these hazards. The cases studied encompass a variety of climatic regimes and stages of economic development, raging across the industrialized countries of Germany, France, Japan and the United States, to Bangladesh, the island nation of Cuba and the mega-city of Shanghai. Demonstrated characteristics of these exemplary cases are synthesized into ten guiding principles for successful early warning systems that will, it is hoped, prove useful to countries seeking to develop or strengthen such systems within their own borders.
The two-volume set LNBIP 353 and 354 constitutes the proceedings of the 22nd International Conference on Business Information Systems, BIS 2019, held in Seville, Spain, in June 2019. The theme of the BIS 2019 was "Data Science for Business Information Systems", inspiring researchers to share theoretical and practical knowledge of the different aspects related to Data Science in enterprises. The 67 papers presented in these proceedings were carefully reviewed and selected from 223 submissions. The contributions were organized in topical sections as follows: Part I: Big Data and Data Science; Artificial Intelligence; ICT Project Management; and Smart Infrastructure. Part II: Social Media and Web-based Systems; and Applications, Evaluations and Experiences.