A Proposed Framework for Big Data Analytics in Higher Education

Mago, B and Khan, N (2021) A Proposed Framework for Big Data Analytics in Higher Education. International Journal of Advanced Computer Science and Applications, 12 (7).

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Students, faculties, and other members of the higher education (HEd) system are increasingly reliant on various information technologies. Such a reliance results in a plethora of data that can be explored to obtain relevant statistics or insights. Another reason to explore the data is to acquire valuable insight regarding the novel unstructured forms of data that are discovered and often found to have a connection with elements of social media such as pictures, videos, Web pages, audio files, etc. Moreover, the data can bring additional valuable benefits when processed in the context of HEd. When used strategically, Big Data (BD) provides educational institutions with the chance to improve the quality of education from all the perspectives and steer students of HEd toward higher rates of completion. Further, this will improve student persistence and results, all of which are facilitated by technology. With this aim, the current research proposes a framework that analyzes the data collected from heterogeneous sources and analyzes using BD analytics tools to do various types of analysis that will be beneficial for different learners, faculties and other members of HEd system. Moreover, current research also focuses on the challenges of acquiring BD from various sources.

Affiliation: Skyline University College
SUC Author(s): Mago, B ORCID: https://orcid.org/0000-0003-1537-1202
All Author(s): Mago, B and Khan, N
Item Type: Article
Uncontrolled Keywords: Big data analysis; higher education; learning analytics; academic analytics
Subjects: B Information Technology > BD Big Data Analitics
Divisions: Skyline University College > School of IT
Depositing User: Mr Veeramani Rasu
Date Deposited: 02 Feb 2022 14:09
Last Modified: 02 Feb 2022 14:09
URI: https://research.skylineuniversity.ac.ae/id/eprint/73
Publisher URL: http://dx.doi.org/10.14569/IJACSA.2021.0120778
Publisher OA policy: https://v2.sherpa.ac.uk/id/publication/19472
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