LEADER 00866nam0-2200289---450- 001 990009943300403321 005 20150223122543.0 035 $a000994330 035 $aFED01000994330 035 $a(Aleph)000994330FED01 035 $a000994330 100 $a20150223d1957----km-y0itay50------ba 101 0 $ager 102 $aDE 105 $ay-------001yy 200 1 $aPericulum locatoris$fMax Kaser 210 $aWeimar$cVerlag Hermann Böhlaus Nachfolger$d1957 215 $ap. 155-200$d24 cm 300 $aEstratto da: Zeitschrift der Savigny-Stiftung für Rechtsgeschichte. 74 Band, romanistische Abteilung 700 1$aKaser,$bMax$f<1906-1997>$0186412 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990009943300403321 952 $aDDR(E)-Kaser-1957 (2)$fDDR 959 $aDDR 996 $aPericulum locatoris$9820877 997 $aUNINA LEADER 04581nam 22006855 450 001 9910720093003321 005 20250610115742.0 010 $a9789819900268$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(CKB)26540740900041 035 $a(EXLCZ)9926540740900041 100 $a20230429d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEducational Data Science: Essentials, Approaches, and Tendencies $eProactive Education based on Empirical Big Data Evidence /$fedited by Alejandro Peña-Ayala 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 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 $aThis book describes theoretical elements, practical approaches, and specialized tools that systematically organize, characterize, and analyze big data gathered from educational affairs and settings. Moreover, the book shows several inference criteria to leverage and produce descriptive, explanatory, and predictive closures to study and understand education phenomena at in classroom and online environments. This is why diverse researchers and scholars contribute with valuable chapters to ground with well-?sounded theoretical and methodological constructs in the novel field of Educational Data Science (EDS), which examines academic big data repositories, as well as to introduces systematic reviews, reveals valuable insights, and promotes its application to extend its practice. EDS as a transdisciplinary field relies on statistics, probability, machine learning, data mining, and analytics, in addition to biological, psychological, and neurological knowledge aboutlearning science. With this in mind, the book is devoted to those that are in charge of educational management, educators, pedagogues, academics, computer technologists, researchers, and postgraduate students, who pursue to acquire a conceptual, formal, and practical landscape of how to deploy EDS to build proactive, real- time, and reactive applications that personalize education, enhance teaching, and improve learning! Chapter ?Sync Ratio and Cluster Heat Map for Visualizing Student Engagement? is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com. 410 0$aBig Data Management,$x2522-0187 606 $aArtificial intelligence$xData processing 606 $aData mining 606 $aBig data 606 $aData Science 606 $aData Mining and Knowledge Discovery 606 $aBig Data 606 $aEducació$2thub 606 $aProcessament de dades$2thub 606 $aDades massives$2thub 608 $aLlibres electrònics$2thub 615 0$aArtificial intelligence$xData processing. 615 0$aData mining. 615 0$aBig data. 615 14$aData Science. 615 24$aData Mining and Knowledge Discovery. 615 24$aBig Data. 615 7$aEducació. 615 7$aProcessament de dades 615 7$aDades massives 676 $a005.7 700 $aPen?a-Ayala$b Alejandro$01355139 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910720093003321 996 $aEducational Data Science: Essentials, Approaches, and Tendencies$94332087 997 $aUNINA