JUCS - Journal of Universal Computer Science 25(13): 1668-1686, doi: 10.3217/jucs-025-13-1668
Adopting Trust in Learning Analytics Infrastructure: A Structured Literature Review
expand article infoGeorge-Petru Ciordas-Hertel‡, Jan Schneider‡, Stefaan Ternier§, Hendrik Drachsler|
‡ DIPF - Leibniz Institute for Research and Information in Education, Frankfurt am Main, Germany§ Open Universiteit, Heerlen| DIPF - Leibniz Institute for Research and Information in Education Frankfurt am Main, Germany, Johann Wolfgang Goethe-Universitat, Germany and Open Universiteit, Heerlen

Corresponding author: George-Petru Ciordas-Hertel ( ciordas@dipf.de )

© George-Petru Ciordas-Hertel, Jan Schneider, Stefaan Ternier, Hendrik Drachsler.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY-ND 4.0). This license allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use.
Citation: Ciordas-Hertel G-P, Schneider J, Ternier S, Drachsler H (2019) Adopting Trust in Learning Analytics Infrastructure: A Structured Literature Review. JUCS - Journal of Universal Computer Science 25(13): 1668-1686. https://doi.org/10.3217/jucs-025-13-1668
Open Access
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).
Keywords
learning analytics, software architecture, infrastructure, big data, trust, data protection, privacy, GDPR, education