Learning Analytics and Survey Data Integration in Workload Research

Beitrag in einer Fachzeitschrift
(Originalarbeit)


Details zur Publikation

Autorinnen und Autoren: Samoilova E, Keusch F, Wolbring T
Zeitschrift: Zeitschrift für Hochschulentwicklung
Jahr der Veröffentlichung: 2017
Band: 12
Heftnummer: 2
Seitenbereich: 65 - 78
ISSN: 0250-6467
Sprache: Englisch


Abstract


While Learning Analytics (LA) has a lot of potential, educators sometimes doubt whether it is worth to invest in the analysis of LA and whether its use yields additional insights. Drawing on data from a pilot study, we illustrate an application of LA for the evaluation of student workload in online or blended learning courses. Although measuring student workload is essential for optimizing learning, workload research is still under development. The study compares results provided by two data sources: viewing activity logs and a weekly evaluation survey. The results indicate that self-reported data provide higher estimates of workload than LA. Moreover, the two measures are only weakly correlated. The results should be replicated with a larger sample size, different sub-populations, and in different contexts.



FAU-Autorinnen und Autoren / FAU-Herausgeberinnen und Herausgeber

Wolbring, Tobias Prof. Dr.
Lehrstuhl für Empirische Wirtschaftssoziologie


Zitierweisen

APA:
Samoilova, E., Keusch, F., & Wolbring, T. (2017). Learning Analytics and Survey Data Integration in Workload Research. Zeitschrift für Hochschulentwicklung, 12(2), 65 - 78. https://dx.doi.org/10.3217/zfhe-12-01/04

MLA:
Samoilova, Evgenia, Florian Keusch, and Tobias Wolbring. "Learning Analytics and Survey Data Integration in Workload Research." Zeitschrift für Hochschulentwicklung 12.2 (2017): 65 - 78.

BibTeX: 

Zuletzt aktualisiert 2018-08-08 um 03:44