LEADER 03764nam 22006015 450 001 9910416090303321 005 20220620045719.0 010 $a3-030-47392-9 024 7 $a10.1007/978-3-030-47392-1 035 $a(CKB)4100000011384180 035 $a(MiAaPQ)EBC6296001 035 $a(DE-He213)978-3-030-47392-1 035 $a(EXLCZ)994100000011384180 100 $a20200810d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdoption of Data Analytics in Higher Education Learning and Teaching /$fedited by Dirk Ifenthaler, David Gibson 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (464 pages) 225 1 $aAdvances in Analytics for Learning and Teaching,$x2662-2122 311 $a3-030-47391-0 327 $aPart I. Theoretical Foundations and Frameworks -- Part II. Technological Infrastructure and Staff Requirements -- Part III. Institutional Governance and Policy Implementation -- Part IV. Case Studies. 330 $aThe book aims to advance global knowledge and practice in applying data science to transform higher education learning and teaching to improve personalization, access and effectiveness of education for all. Currently, higher education institutions and involved stakeholders can derive multiple benefits from educational data mining and learning analytics by using different data analytics strategies to produce summative, real-time, and predictive or prescriptive insights and recommendations. Educational data mining refers to the process of extracting useful information out of a large collection of complex educational datasets while learning analytics emphasizes insights and responses to real-time learning processes based on educational information from digital learning environments, administrative systems, and social platforms. This volume provides insight into the emerging paradigms, frameworks, methods and processes of managing change to better facilitate organizational transformation toward implementation of educational data mining and learning analytics. It features current research exploring the (a) theoretical foundation and empirical evidence of the adoption of learning analytics, (b) technological infrastructure and staff capabilities required, as well as (c) case studies that describe current practices and experiences in the use of data analytics in higher education. 410 0$aAdvances in Analytics for Learning and Teaching,$x2662-2122 606 $aEducational technology 606 $aLearning 606 $aInstruction 606 $aEducation, Higher 606 $aEducational Technology$3https://scigraph.springernature.com/ontologies/product-market-codes/O21000 606 $aLearning & Instruction$3https://scigraph.springernature.com/ontologies/product-market-codes/O22000 606 $aHigher Education$3https://scigraph.springernature.com/ontologies/product-market-codes/O36000 615 0$aEducational technology. 615 0$aLearning. 615 0$aInstruction. 615 0$aEducation, Higher. 615 14$aEducational Technology. 615 24$aLearning & Instruction. 615 24$aHigher Education. 676 $a378.007 676 $a378.007 702 $aIfenthaler$b Dirk$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGibson$b David$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910416090303321 996 $aAdoption of Data Analytics in Higher Education Learning and Teaching$92057313 997 $aUNINA