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Record Nr. |
UNINA9910416090303321 |
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Titolo |
Adoption of Data Analytics in Higher Education Learning and Teaching / / edited by Dirk Ifenthaler, David Gibson |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (464 pages) |
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Collana |
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Advances in Analytics for Learning and Teaching, , 2662-2122 |
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Disciplina |
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Soggetti |
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Educational technology |
Learning |
Instruction |
Education, Higher |
Educational Technology |
Learning & Instruction |
Higher Education |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Part I. Theoretical Foundations and Frameworks -- Part II. Technological Infrastructure and Staff Requirements -- Part III. Institutional Governance and Policy Implementation -- Part IV. Case Studies. |
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Sommario/riassunto |
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The 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, |
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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. |
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