Post-GDPR usage of students' 'Big-data' at UK Universities

FEARN, Carolyn and KOYA, Kushwanth (2021). Post-GDPR usage of students' 'Big-data' at UK Universities. In: TOEPPE, Katharina, YAN, Hui and CHU, Samuel Kai Wah, (eds.) Diversity, Divergence, Dialogue. Lecture Notes in Computer Science, 12645 . Springer Verlag, 165-182.

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Official URL: https://link.springer.com/chapter/10.1007/978-3-03...
Link to published version:: https://doi.org/10.1007/978-3-030-71292-1_15
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    Abstract

    Higher education institutions are extensively using students’ big-data to develop student services, create management or staff-led interventions and inform their strategic decisions etc. Following the implementation of the European Union’s General Data Protection Regulation (GDPR) in 2018, there has been extensive uncertainty regarding the use of students’ data. By conducting interviews with various University staff in the UK, this research aims to explore their understanding and usage of students’ data, post-GDPR implementation. The findings indicate students’ data is primarily used to build learning analytic tools and student-retention activities. Additionally, it was found that the understanding and usage of both big-data and GDPR differed across various Universities’ stakeholders, and there is inadequate support available to these stakeholders. Overall, this research indicates the adoption of big-data based learning analytics requires comprehensive development and implementation policies to address the challenges of learning analytics. Therefore, this research proposes such an approach through co-creation with staff and students; institutional research and staff training.

    Item Type: Book Section
    Additional Information: Lecture Notes in Computer Science
    Uncontrolled Keywords: Artificial Intelligence & Image Processing
    Identification Number: https://doi.org/10.1007/978-3-030-71292-1_15
    Page Range: 165-182
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 21 Dec 2020 12:42
    Last Modified: 19 May 2021 08:52
    URI: http://shura.shu.ac.uk/id/eprint/27853

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