LEADER 03708nam 2200505 450 001 996547967203316 005 20230801215613.0 010 $a9789819900268$b(electronic bk.) 010 $z9789819900251 024 7 $a10.1007/978-981-99-0026-8 035 $a(MiAaPQ)EBC7243092 035 $a(Au-PeEL)EBL7243092 035 $a(DE-He213)978-981-99-0026-8 035 $a(OCoLC)1378936602 035 $a(PPN)26965819X 035 $a(EXLCZ)9926540740900041 100 $a20230801d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEducational data science $eessentials, approaches, and tendencies /$fAlejandro Pen?a-Ayala 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore Pte Ltd,$d[2023] 210 4$d©2023 215 $a1 online resource (298 pages) 225 1 $aBig Data Management,$x2522-0187 311 08$aPrint version: Peña-Ayala, Alejandro Educational Data Science: Essentials, Approaches, and Tendencies Singapore : Springer,c2023 9789819900251 320 $aIncludes bibliographical references and index. 327 $a1. Engaging in Student-Centered Educational Data Science through Learning Engineering -- 2. A review of clustering models in educational data science towards fairness-aware learning -- 3. Educational Data Science: Is an ?Umbrella Term? or an Emergent Domain? -- 4. Educational Data Science Approach for End-to-End Quality Assurance Process for Building Credit-Worthy Online Courses -- 5. Understanding the Effect of Cohesion in Academic Writing Clarity Using Education Data Science -- 6. Sequential pattern mining in educational data: the application context, potential, strengths, and limitations -- 7. Sync Ratio and Cluster Heat Map for Visualizing Student Engagement. 330 $aThis book describes theoretical elements, practical approaches, and specialized tools that systematically organize, characterize, and analyze big data gathered from educational affairs and settings. Moreover, the book shows several inference criteria to leverage and produce descriptive, explanatory, and predictive closures to study and understand education phenomena at in classroom and online environments. This is why diverse researchers and scholars contribute with valuable chapters to ground with well-?sounded theoretical and methodological constructs in the novel field of Educational Data Science (EDS), which examines academic big data repositories, as well as to introduces systematic reviews, reveals valuable insights, and promotes its application to extend its practice. EDS as a transdisciplinary field relies on statistics, probability, machine learning, data mining, and analytics, in addition to biological, psychological, and neurological knowledge about learning science. With this in mind, the book is devoted to those that are in charge of educational management, educators, pedagogues, academics, computer technologists, researchers, and postgraduate students, who pursue to acquire a conceptual, formal, and practical landscape of how to deploy EDS to build proactive, real- time, and reactive applications that personalize education, enhance teaching, and improve learning! 410 0$aBig Data Management,$x2522-0187 606 $aBig data 606 $aEducation$xData processing 615 0$aBig data. 615 0$aEducation$xData processing. 676 $a005.7 700 $aPen?a-Ayala$b Alejandro$01355139 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a996547967203316 996 $aEducational data science$93417215 997 $aUNISA