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024 7 _a10.1007/978-981-99-0026-8
_2doi
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072 7 _aUN
_2bicssc
072 7 _aCOM021000
_2bisacsh
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082 0 4 _a005.7
_223
245 1 0 _aEducational Data Science: Essentials, Approaches, and Tendencies
_h[electronic resource] :
_bProactive Education based on Empirical Big Data Evidence /
_cedited by Alejandro Peña-Ayala.
250 _a1st ed. 2023.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2023.
300 _aXIII, 291 p. 1 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aBig Data Management,
_x2522-0187
505 0 _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.
520 _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!
650 0 _aArtificial intelligence
_xData processing.
650 0 _aData mining.
650 0 _aBig data.
650 1 4 _aData Science.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aBig Data.
700 1 _aPeña-Ayala, Alejandro.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819900251
776 0 8 _iPrinted edition:
_z9789819900275
776 0 8 _iPrinted edition:
_z9789819900282
830 0 _aBig Data Management,
_x2522-0187
856 4 0 _uhttps://doi.org/10.1007/978-981-99-0026-8
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c173441
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