Big Data Academic and Learning Analytics: Connecting the Dots for Academic Excellence in Higher Education

Autor/inn/en	Chaurasia, Sushil S.; Kodwani, Devendra; Lachhwani, Hitendra; Ketkar, Manisha Avadhut
Titel	Big Data Academic and Learning Analytics: Connecting the Dots for Academic Excellence in Higher Education
Quelle	In: International Journal of Educational Management, 32 (2018) 6, S.1099-1117 (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Chaurasia, Sushil S.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0951-354X
DOI	10.1108/IJEM-08-2017-0199
Schlagwörter	Data Analysis; Higher Education; Academic Achievement; Excellence in Education; Educational Administration; College Administration; Attribution Theory; Compliance (Legal); Competition; Case Studies; Risk; Models; Educational Change; Business; Futures (of Society); Information Security; Privacy + Suchen Sie Ihr Suchwort? Auswertung; Hochschulbildung; Hochschulsystem; Hochschulwesen; Schulleistung; Lernerfolg; Bildungsverwaltung; Schuladministration; Schulverwaltung; College administrators; Hochschulverwaltung; Wettkampf; Case study; Fallstudie; Case Study; Risiko; Analogiemodell; Bildungsreform; Business studies; Wirtschaft; Betriebswirtschaft; Future; Society; Zukunft; Privatsphäre
Abstract	Purpose: Although big data analytics (BDA) have great benefits for higher education institutions (HEIs), due to lack of sufficient evidence on how BDA investment can pay off, it is tough for HEIs practitioners to realize value from such adoption. The purpose of this paper is to propose a big data academic and learning analytics enabled business value model to explain BDA potential benefits and business value which can be obtained by developing such analytics capabilities in HEIs. Design/methodology/approach: The study examined 47 case descriptions from 26 HEIs to investigate the causal association between the BDA current and potential benefits and business value creation path for big data academic and learning analytics success in HEIs. Findings: The pressure of compliance with all legal and regulatory requirements and competition had pushed HEIs hard to adopt BDA tools. However, the study found out that application of risk and security and predictive analytics to higher education fields is still in its infancy. Using this theoretical model, the results provide new insights to higher education administrators on ways to create BDA capabilities for HEIs transformation and suggest an empirical foundation that can lead to more thorough analysis of BDA implementation. Originality/value: A distinctive theoretical contribution of this study is its conceptualization of understanding business value from BDA in the typical setting of higher education. The study provides HEIs with an all-inclusive understanding of BDA and gives insights on how it helps to transform HEIs. The new perspectives associated with the big data academic and learning analytics enabled business value model will contribute to future research in this area. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01