Educational Data Science Essentials Approaches And Tendencies Proactive Education Based On Empirical Big Data Evidence Alejandro Peaayala by Alejandro Peña-ayala 9789819900268, 9819900263 instant download after payment.

This book describes theoretical elements, practical approaches, and specialized tools that systematically organize, characterize, and analyze Big Data gathered from educational affairs and settings. Moreover, the book shows several inference criteria to leverage and produce descriptive, explanatory, and predictive closures to study and understand education phenomena at in classroom and online environments.

This is why diverse researchers and scholars contribute with valuable chapters to ground with well-–sounded theoretical and methodological constructs in the novel field of Educational Data Science (EDS), which examines academic Big Data repositories, as well as to introduces systematic reviews, reveals valuable insights, and promotes its application to extend its practice.

EDS as a transdisciplinary field relies on 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 devoted to those that are in charge of educational management, educators, pedagogues, academics, computer technologists, researchers, and postgraduate students, who pursue to acquire a conceptual, formal, and practical landscape of how to deploy EDS to build proactive, real- time, and reactive applications that personalize education, enhance teaching, and improve learning!

As a result of the broad use of computer systems, the spread of Internet applications, and the deployment of Artificial Intelligence approaches in a wide variety of human daily life matters, among other technological trends, an accelerated datafication of digital data spreads worldwide. Such a revolutionary dynamic wave has demanded the emergence of novel paradigms to gather, storage, manage, and examine huge volume of distributed data that hides rich information and knowledge that is valuable and useful to lead the development of data-driven solutions.

In this context, Data Science is one of those disruptive domains that leverages the best of mature disciplines (e.g., statistics, data analysis, and Machine Learning) to build a baseline, which grounds specialized approaches that focus on data, as the main target of study to characterize, analyze, and interpret the phenology of symbolic representations of abstract and concrete entities and events that live and occur in the world and outer space. Hence, Data Science appears as part of the arrowhead of novel technologies, whose strength relies on the power to give meaning to the big data collected from diverse human activity fields as the education.