Adopting Trust in Learning Analytics Infrastructure: A Structured Literature Review

George-Petru Ciordas-Hertel, Jan Schneider, S.G.C. Ternier, H.J. Drachsler

    Research output: Contribution to journalArticlepeer-review

    Abstract

    One key factor for the successful outcome of a Learning Analytics (LA) infrastructure is the ability to decide which software architecture concept is necessary. Big Data can be used to face the challenges LA holds. Additional challenges on privacy rights are introduced to the Europeans by the General Data Protection Regulation (GDPR). Beyond that, the challenge of how to gain the trust of the users remains. We found diverse architectural concepts in the domain of LA. Selecting an appropriate solution is not straightforward. Therefore, we conducted a structured literature review to assess the state-of-the-art and provide an overview of Big Data architectures used in LA. Based on the examination of the results, we identify common architectural components and technologies and present them in the form of a mind map. Linking the findings, we are proposing an initial approach towards a Trusted and Interoperable Learning Analytics Infrastructure (TIILA).
    Original languageEnglish
    Pages (from-to)1668-1686
    Number of pages19
    JournalJournal of Universal Computer Science
    Volume25
    Issue number13
    DOIs
    Publication statusPublished - 28 Dec 2019

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