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000777112 019__ $$a957610132
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000777112 08204 $$a378.0072$$223
000777112 24500 $$aBig data and learning analytics in higher education :$$bcurrent theory and practice /$$cBen Kei Daniel, editor.
000777112 264_1 $$aSwitzerland :$$bSpringer,$$c[2017].
000777112 264_4 $$c©2017
000777112 300__ $$a1 online resource.
000777112 336__ $$atext$$btxt$$2rdacontent
000777112 337__ $$acomputer$$bc$$2rdamedia
000777112 338__ $$aonline resource$$bcr$$2rdacarrier
000777112 504__ $$aIncludes bibliographical references index.
000777112 506__ $$aAccess limited to authorized users.
000777112 520__ $$aThis book focuses on the uses of big data in the context of higher education. The book describes a wide range of administrative and operational data gathering processes aimed at assessing institutional performance and progress in order to predict future performance, and identifies potential issues related to academic programming, research, teaching and learning. Big data refers to data which is fundamentally too big and complex and moves too fast for the processing capacity of conventional database systems. The value of big data is the ability to identify useful data and turn it into useable information by identifying patterns and deviations from patterns.
000777112 588__ $$aOnline resource, title from PDF title page (viewed on September 7, 2016).
000777112 650_0 $$aEducation, Higher$$xResearch.
000777112 650_0 $$aBig data.
000777112 7001_ $$aDaniel, Ben Kei,$$d1971-$$eeditor.
000777112 77608 $$iPrint version:$$tBig data and learning analytics in higher education.$$dSwitzerland : Springer, [2017]$$z331906519X$$w(OCoLC)962055750
000777112 852__ $$bebk
000777112 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-06520-5$$zOnline Access$$91397441.1
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000777112 982__ $$aEbook
000777112 983__ $$aOnline
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