Learning analytics and survey data integration in workload research

Samoilova, Evgenia ; Keusch, Florian ; Wolbring, Tobias

DOI: https://doi.org/10.3217/zfhe-12-01/04
URL: https://www.researchgate.net/publication/316068672...
Additional URL: https://www.zfhe.at/index.php/zfhe/article/view/99...
Document Type: Article
Year of publication: 2017
The title of a journal, publication series: Zeitschrift für Hochschulentwicklung : ZFHE
Volume: 12
Issue number: 1
Page range: 65-78
Place of publication: Wien
Publishing house: Österreichische Gesellschaft für Hochschuldidaktik
ISSN: 2219-6994
Publication language: English
Institution: Außerfakultäre Einrichtungen > Mannheim Centre for European Social Research - Research Department A
School of Social Sciences > Statistik u. Sozialwiss. Methodenlehre (Juniorprofessur) (Keusch 2016-2021)
School of Social Sciences > Soziologie, insbes. Längsschnittdatenanalyse (Juniorprofessur) (Wolbring 2015-2017)
Subject: 300 Social sciences, sociology, anthropology
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.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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