276
Views
2
CrossRef citations to date
0
Altmetric
Articles

German and Swedish students going digital: do gender and interaction matter in quality evaluation of digital learning systems?

ORCID Icon &
Pages 4367-4381 | Received 22 Dec 2019, Accepted 21 Jul 2021, Published online: 14 Aug 2021
 

ABSTRACT

This study aims to examine the difference in students’ satisfaction with the Quality Characteristics (QualChar) of the Digital Learning Systems (DLs) with regards to gender and frequency of interaction between students-students and students-teachers. A cross-sectional online quantitative survey was used to collect data from English language business administration and social sciences online master’ students at eight German and Swedish Universities. The t-test shows that in both German and Swedish settings male students are more satisfied with the QualChar of DLs. Further analysis of multidimensional QualChar shows that German male students are more satisfied with the intrinsic and contextual QualChar of DLs – contextual, accessible, and actionable QualChar of DLs are important for Swedish male students. Concerning students’ interaction frequency with teachers and students, the ANOVA test revealed a statistical difference in the German students’ satisfaction with QualChar of DLs – while no difference was found for Swedish students. German student respondents are active in their interaction with teachers and other students which is associated with their satisfaction with QualChar of DLs. This study contributes to the literature in shedding light on the German and Swedish students’ satisfaction with the QualChar of the DLs highlighting the differences by gender and interaction frequency.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Alexander von Humboldt-Stiftung, Germany.

Notes on contributors

Mehwish Waheed

Mehwish Waheed (PhD) is a Post-Doctorate at Ca’Foscari University, Italy. Her research interests focus on knowledge quality, industry 4.0, digital transformation, information systems, innovation adoption, innovative behavior, electronic learning. Her research work publishes in the Decision Support System, Information Technology & People, Journal of Computer Assisted Learning, Internet Research, and Behaviour & Information Technology. She is also a member of the Editorial Advisory board of the Global Journal of Business Social Sciences Review. Prior, she served as a Post-Doc at Technische Universität, Dortmund, Germany and a research associate at Asia-Europe Institute at the University of Malaya, Malaysia.

Liudvika Leišytė

Liudvika Leišytė (PhD) is Professor of Higher Education and Vice-Director of the Center for Higher Education at the Technical University of Dortmund in Germany and is a visiting senior scholar at the Center for Higher Education Policy Studies, the University of Twente in the Netherlands. Her research focuses on academic work and organizational transformation in the context of changing institutional environment, especially focusing on the questions of professional autonomy, quality evaluation, academic productivity and gender equality. In 2018 she received the Emerald Literati Award for the highly commended paper in the Learning Organization journal. She is a member of editorial boards of Higher Education Policy, Triple Helix, Organizational Learning, Social Inclusion, Acta Pedagogica Vilnensis and the European Journal of Higher Education.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 296.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.