1,952
Views
17
CrossRef citations to date
0
Altmetric
Articles

Towards a methodology to determine standard time allocations for academic work

&
Pages 503-523 | Published online: 20 Jul 2017
 

ABSTRACT

An online survey of workload activities was circulated to academics across Australia seeking estimates for the time to undertake a range of academic-related tasks associated with teaching, research and service. This article summarises the most important findings from the teaching data of the 2059 respondents. This detail of workload data has not been reported before across the Australian university sector. The findings showing that most academics work more than 50 h per week are consistent with previous studies. Although the estimates of the individuals varied greatly, statistical inquiry indicated the median time required does not vary by experience and online teaching generally requires more preparation time than on-campus teaching. The paper proposes this methodology as a credible means to derive realistic time-based standards for other aspects of academic work and will assist university managers by providing an external benchmark upon which to develop local academic workload models.

Acknowledgements

The authors would like to thank the National Tertiary Education Union (NTEU) for supporting this study.

Institutional ethics approval number H0010977.

Disclosure statement

No potential conflict of interest was reported by the authors.

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 404.00 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.