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Comprises of 8 books for grade 1 to 8
Comprises of 8 books for grade 1 to 8
Comprises of 8 books for grade 1 to 8
This edited volume fills the gaps in existing literature on visualization and dashboard design for learning analytics. To do so, it presents critical tips to stakeholders and acts as guide to efficient implementation. The book covers the following topics: visualization and dashboard design for learning analytics, visualization and dashboard preferences of stakeholders, learners’ patterns on the dashboard, usability of visualization techniques and the dashboard, dashboard and intervention design, learning and instructional design for learning analytics, privacy and security issues about the dashboard, and future directions of visualization and dashboard design. This book will be of interest to researchers with interest in learning analytics and data analytics, teachers and students in higher education institutions and instructional designers, as it includes contributions from a wide variety of educational and psychological researchers, engineers, instructional designers, learning scientists, and computer scientists interested in learning analytics.
The Social Dynamics of Open Data is a collection of peer reviewed papers presented at the 2nd Open Data Research Symposium (ODRS) held in Madrid, Spain, on 5 October 2016. Research is critical to developing a more rigorous and fine-combed analysis not only of why open data is valuable, but how it is valuable and under what specific conditions. The objective of the Open Data Research Symposium and the subsequent collection of chapters published here is to build such a stronger evidence base. This base is essential to understanding what open datas impacts have been to date, and how positive impacts can be enabled and amplified. Consequently, common to the majority of chapters in this collection is the attempt by the authors to draw on existing scientific theories, and to apply them to open data to better explain the socially embedded dynamics that account for open datas successes and failures in contributing to a more equitable and just society.
An encyclopedia designed especially to meet the needs of elementary, junior high, and senior high school students.
In the age of rapid technological advancements, the integration of Artificial Intelligence (AI), machine learning (ML), and large language models (LLMs) in Science, Technology, Engineering, and Mathematics (STEM) education has emerged as a transformative force, reshaping pedagogical approaches and assessment methodologies. Uses of AI in STEM Education, comprising 25 chapters, delves deep into the multifaceted realm of AI-driven STEM education. It begins by exploring the challenges and opportunities of AI-based STEM education, emphasizing the intricate balance between human tasks and technological tools. As the chapters unfold, readers learn about innovative AI applications, from automated scoring systems in biology, chemistry, physics, mathematics, and engineering to intelligent tutors and adaptive learning. The book also touches upon the nuances of AI in supporting diverse learners, including students with learning disabilities, and the ethical considerations surrounding AI's growing influence in educational settings. It showcases the transformative potential of AI in reshaping STEM education, emphasizing the need for adaptive pedagogical strategies that cater to diverse learning needs in an AI-centric world. The chapters further delve into the practical applications of AI, from scoring teacher observations and analyzing classroom videos using neural networks to the broader implications of AI for STEM assessment practices. Concluding with reflections on the new paradigm of AI-based STEM education, this book serves as a comprehensive guide for educators, researchers, and policymakers, offering insights into the future of STEM education in an AI-driven world.
Data Science and Analytics explores the application of big data and business analytics by academics, researchers, industrial experts, policy makers and practitioners, helping the reader to understand how big data can be efficiently utilized in better managerial applications.
This book enhances the reader’s understanding of the theoretical foundations, sociotechnical assemblage, and governance mechanisms of sustainable smart city transitions. Drawing on empirical evidence stemming from existing smart city research, the book begins by advancing a theory of sustainable smart city transitions, which forms bridges between smart city development studies and some of the key assumptions underpinning transition management and system innovation research, human geography, spatial planning, and critical urban scholarship. This interdisciplinary theoretical formulation details how smart city transitions unfold and how they should be conceptualized and enacted in order to be assembled as sustainable developments. The proposed theory of sustainable smart city transitions is then enriched by the findings of investigations into the planning and implementation of smart city transition strategies and projects. Focusing on different empirical settings, change dimensions, and analytical elements, the attention moves from the sociotechnical requirements of citywide transition pathways to the development of sector-specific smart city projects and technological innovations, in particular in the fields of urban mobility and urban governance. This book represents a relevant reference work for academic and practitioner audiences, policy makers, and representative of smart city industries. The chapters in this book were originally published as a special issue of the Journal of Urban Technology.