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!
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