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001 978-3-030-47392-1
003 DE-He213
005 20210226030609.0
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020 _a9783030473921
_9978-3-030-47392-1
024 7 _a10.1007/978-3-030-47392-1
_2doi
050 4 _aLC8-6691
072 7 _aJNV
_2bicssc
072 7 _aEDU039000
_2bisacsh
072 7 _aJNV
_2thema
082 0 4 _a371.33
_223
245 1 0 _aAdoption of Data Analytics in Higher Education Learning and Teaching
_h[electronic resource] /
_cedited by Dirk Ifenthaler, David Gibson.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aXXXVIII, 434 p. 104 illus., 74 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdvances in Analytics for Learning and Teaching,
_x2662-2122
505 0 _aPart I. Theoretical Foundations and Frameworks -- Part II. Technological Infrastructure and Staff Requirements -- Part III. Institutional Governance and Policy Implementation -- Part IV. Case Studies.
520 _aThe book aims to advance global knowledge and practice in applying data science to transform higher education learning and teaching to improve personalization, access and effectiveness of education for all. Currently, higher education institutions and involved stakeholders can derive multiple benefits from educational data mining and learning analytics by using different data analytics strategies to produce summative, real-time, and predictive or prescriptive insights and recommendations. Educational data mining refers to the process of extracting useful information out of a large collection of complex educational datasets while learning analytics emphasizes insights and responses to real-time learning processes based on educational information from digital learning environments, administrative systems, and social platforms. This volume provides insight into the emerging paradigms, frameworks, methods and processes of managing change to better facilitate organizational transformation toward implementation of educational data mining and learning analytics. It features current research exploring the (a) theoretical foundation and empirical evidence of the adoption of learning analytics, (b) technological infrastructure and staff capabilities required, as well as (c) case studies that describe current practices and experiences in the use of data analytics in higher education.
650 0 _aEducational technology.
650 0 _aLearning.
650 0 _aInstruction.
650 0 _aHigher education.
650 1 4 _aEducational Technology.
_0https://scigraph.springernature.com/ontologies/product-market-codes/O21000
650 2 4 _aLearning & Instruction.
_0https://scigraph.springernature.com/ontologies/product-market-codes/O22000
650 2 4 _aHigher Education.
_0https://scigraph.springernature.com/ontologies/product-market-codes/O36000
700 1 _aIfenthaler, Dirk.
_eeditor.
_0(orcid)0000-0002-2446-6548
_1https://orcid.org/0000-0002-2446-6548
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aGibson, David.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030473914
776 0 8 _iPrinted edition:
_z9783030473938
776 0 8 _iPrinted edition:
_z9783030473945
830 0 _aAdvances in Analytics for Learning and Teaching,
_x2662-2122
856 4 0 _uhttps://doi.org/10.1007/978-3-030-47392-1
912 _aZDB-2-EDA
912 _aZDB-2-SXED
999 _c102246
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