Gender Bias in Student Evaluations of Teaching: ‘Punish[ing] Those Who Fail To Do Their Gender Right’

A significant body of work problematises the assumption that student evaluations of teaching (SET) actually measure teaching quality. This is concerning, given that SET are increasingly relied upon not only to evaluate candidates for employment (so job acquisition is influenced by flawed data) but also to inform performance metrics for those in employment (so job security is influenced by flawed data). This paper presents qualitative research conducted at a large public university in Australia. The findings suggest that student evaluations of teaching seem to measure conformity with gendered expectations rather than teaching quality, with particularly negative effects for women. The integration of SET into performance management practices within institutions of higher education could be entrenching inequalities amongst university staff that could ultimately disadvantage female academics.

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Notes

Future research might fruitfully explore disciplinary differences in the qualitative data to which we had access. This was not within the scope of the present study. Quantitative analysis of the data ‘detected statistically significant bias against women and staff with non-English language backgrounds’ that varied across faculties (Fan et al., 2019, p. 14). Fan et al.’s study suggested that descriptive representation (based on identity markers such as gender and language group) is important in combatting bias; ‘where there are larger proportions of female teachers, such as in the Arts and Social Sciences, there is less gender bias in student evaluations of teaching. In Science, where the largest proportion of staff are male English speakers, we have observed stronger biases against the minority groups’ (Fan et al., 2019, p. 11).

The concept of prevalence is tricky in qualitative analysis, as it carries connotations of quantitative evaluation; as Braun and Clarke explain, ‘[i]t is not the case that if it was present in 50% of one’s data items, it would be a theme, but if it was present only in 47%, then it would not be a theme’; thus, researcher judgement regarding the qualities of the data is key (Braun & Clarke 2006, p. 82).

One example of a discarded theme was ‘offensive comments’ (though it is notable that in the sample these were more prevalent in the comments pertaining to female-identified teachers than male), while another was ‘odd/incomprehensible’.

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Author information

Authors and Affiliations

1. School of Humanities and Languages, UNSW Sydney, Sydney, NSW, 2052, Australia Sophie Adams
2. Centre for Qualitative Research, University of Bath, Bath, UK Sheree Bekker
3. School of Mathematics and Statistics, UNSW Sydney, Sydney, NSW, 2052, Australia Yanan Fan & Eve Slavich
4. Mark Wainwright Analytical Centre, UNSW Sydney, Sydney, NSW, 2052, Australia Tess Gordon
5. Department of Government and International Relations, The University of Sydney, Sydney, NSW, 2052, Australia Laura J. Shepherd
6. DVC (Research) Division, University of Queensland, QLD, Brisbane, 4072, Australia David Waters

1. Sophie Adams