{"product_id":"educational-data-science-essentials-approaches-and-tendencies-proactive-education-based-on-empirical-big-data-evidence-9789819900251","title":"Educational Data Science: Essentials, Approaches, and Tendencies: Proactive Education based on Empirical Big Data Evidence","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003eThis book provides a comprehensive overview of theoretical elements, practical approaches, and specialized tools for organizing, characterizing, and analyzing big data in educational affairs and settings. It offers inference criteria for studying and understanding education phenomena in classroom and online environments. The field of Educational Data Science (EDS) utilizes statistics, probability, machine learning, data mining, and analytics, along with biological, psychological, and neurological knowledge, to develop personalized education, enhance teaching, and improve learning. It is aimed at educators, managers, researchers, and students interested in deploying EDS to build proactive, real-time, and reactive applications. \u003c\/blockquote\u003e\u003cp\u003e\u003cstrong\u003eFormat\u003c\/strong\u003e: Hardback\u003cbr\u003e\u003cstrong\u003eLength\u003c\/strong\u003e: 291 pages\u003cbr\u003e\u003cstrong\u003ePublication date\u003c\/strong\u003e: 30 April 2023\u003cbr\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: Springer Verlag, Singapore\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cbr\u003eThis comprehensive book delves into the realm of theoretical foundations, practical methodologies, and specialized tools for systematically organizing, characterizing, and analyzing large datasets derived from educational affairs and settings. Moreover, it presents a range of inference criteria to leverage and generate descriptive, explanatory, and predictive conclusions to study and understand education phenomena in both classroom and online environments.\u003cbr\u003e\u003cbr\u003eThe significance of this book lies in its ability to bring together a diverse array of researchers and scholars who contribute valuable chapters that establish robust theoretical and methodological frameworks in the emerging field of Educational Data Science (EDS). EDS focuses on examining academic big data repositories and employs systematic reviews to uncover valuable insights and promote its practical application.\u003cbr\u003e\u003cbr\u003eAs a transdisciplinary field, EDS draws upon statistics, probability, machine learning, data mining, and analytics, in addition to biological, psychological, and neurological knowledge about learning science. With this in mind, the book is tailored to those who hold positions of responsibility in educational management, educators, pedagogues, academics, computer technologists, researchers, and postgraduate students. These individuals are eager to acquire a comprehensive conceptual, formal, and practical understanding of how to deploy EDS to develop proactive, real-time, and reactive applications that personalize education, enhance teaching, and improve learning outcomes.\u003cbr\u003e\u003cbr\u003eThe book is organized into several chapters, each dedicated to exploring different aspects of EDS. The first chapter provides an introduction to the field, highlighting its importance and potential applications. It explores the challenges and opportunities associated with analyzing educational data and discusses the key concepts and methodologies employed in EDS.\u003cbr\u003e\u003cbr\u003eThe subsequent chapters delve into specific topics within EDS. For instance, Chapter 2 explores the theoretical foundations of big data analysis, including concepts such as data types, sampling techniques, and data visualization. It also discusses the ethical considerations and privacy concerns associated with collecting and analyzing educational data.\u003cbr\u003e\u003cbr\u003eChapter 3 focuses on practical approaches to organizing and analyzing educational data. It discusses the use of data warehouses, data mining techniques, and machine learning algorithms to extract meaningful insights from large datasets. The chapter also highlights the importance of data quality assurance and data cleaning to ensure the accuracy and reliability of the analysis.\u003cbr\u003e\u003cbr\u003eChapter 4 explores the application of EDS in educational settings. It discusses the use of data-driven decision-making to improve teaching and learning outcomes. The chapter also explores the role of predictive analytics and machine learning algorithms to identify patterns and trends\u003cbr\u003e\u003cbr\u003eIn conclusion, this book serves as a valuable resource for educators, researchers, and practitioners seeking to harness the power of big data to personalize education, enhance teaching, and improve learning outcomes. By providing a comprehensive and interdisciplinary approach to EDS, it enables readers to gain a deep understanding of the theoretical foundations, practical methodologies, and specialized tools necessary to effectively analyze and interpret educational data. The book's contributions will undoubtedly pave the way for further advancements in the field and have a significant impact on the future of education.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e: 629g\u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 235 x 155 (mm)\u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9789819900251\u003cbr\u003e \u003cstrong\u003eEdition number\u003c\/strong\u003e: 1st ed. 2023\u003c\/p\u003e","brand":"Shulph Ink","offers":[{"title":"Hardback","offer_id":44316437086458,"sku":"9789819900251","price":124.94,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/noImage_1_31bb87c9-ed6d-4715-955c-a014a40f10f4.jpg?v=1688472044","url":"https:\/\/shulphink.com\/products\/educational-data-science-essentials-approaches-and-tendencies-proactive-education-based-on-empirical-big-data-evidence-9789819900251","provider":"Shulph Ink","version":"1.0","type":"link"}