TY - JOUR
T1 - Adopting Trust in Learning Analytics Infrastructure
T2 - A Structured Literature Review
AU - Ciordas-Hertel, George-Petru
AU - Schneider, Jan
AU - Ternier, S.G.C.
AU - Drachsler, H.J.
N1 - Ciordas-Hertel, G., P., Schneider, J., Ternier, S., Drachsler, H., (2020). Adopting Trust in Learning Analytics Infrastructure: A Structured Literature Review. Journal of Universal Computer Science, Vol. 25, No. 13, pp. 1668-1686.
PY - 2019/12/28
Y1 - 2019/12/28
N2 - 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).
AB - 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).
U2 - 10.3217/jucs-025-13-1668
DO - 10.3217/jucs-025-13-1668
M3 - Article
VL - 25
SP - 1668
EP - 1686
JO - Journal of Universal Computer Science
JF - Journal of Universal Computer Science
SN - 0948-695X
IS - 13
ER -