2,050
Views
67
CrossRef citations to date
0
Altmetric
Original Articles

Characterising a teaching and learning environment conducive to making demands on students while not making their workload excessive

&
Pages 185-198 | Published online: 24 Jan 2007
 

Abstract

A qualitative study of perception of workload found that it was very weakly related to hours of work. The complex construct was better characterised as being influenced by a broadly conceived teaching and learning environment. It appeared to be possible to encourage students to perform a great deal of high‐quality work, without complaining about excessive workload, by attention to this environment. This hypothesis was tested quantitatively with structural equation modelling with a sample of 3320 undergraduate students at a university in Hong Kong. The hypothesised model had nine factors of the teaching and learning environment grouped under three higher‐order latent variables: teaching, teacher–student relationships and student–student relationships which have influences on perceived workload. The model showed a good fit to the data, confirming the hypothesis that attention to the teaching and learning environment can spur students to work hard without feeling overly stressed. The questionnaire could be used as a diagnostic tool to discover which aspects of the environment need attention.

Acknowledgment

The data used for this study was from a project funded by the University Grants Council of Hong Kong.

Notes

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 678.00 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.