Gender Stereotypes in the Text of Teaching Excellence Submissions
Published Jan 10, 2022 · Updated Mar 08, 2026
What if teaching excellence is not judged against the same standard for every member of staff? This paper shows how gender stereotypes can shape who students nominate as excellent, and which qualities they notice once they do.
At Student Voice AI, one of our guiding aims is to make better use of the free-text data universities already collect, so all groups of students are heard. A crucial part of that work is identifying and quantifying bias in comments and nominations, especially when those signals influence awards, evaluation, and perceptions of quality, which is why a structured approach to student evaluation data matters.
This paper investigated gendered differences in student-submitted nominations for an excellence award at a mid-sized university in England. The authors use shifting standards theory, the idea that evaluative standards can move because of stereotypes, to interpret the pattern in the data.
The study asks two practical questions: does gender affect a student's likelihood of nominating a teacher, and what do students count as excellent teaching when they make that choice? For institutions using awards or other student evaluations of teaching to recognise teaching quality, that question matters because it shapes who gets seen.
The study found that students described excellent teachers as engaging, inspiring, motivating, friendly, charismatic, and organised.
Those qualities clearly matter to students. The caution is that they do not automatically identify the most effective educators. If institutions treat them as neutral markers of excellence, they risk rewarding traits students expect to see, rather than the full range of behaviours that support learning well.
Teaching excellence has long been difficult to define. Broad principles are possible, but no single definition works equally well across every subject, cohort, and classroom context. Once excellence is treated as objective, it becomes easier to miss how social expectations shape who is recognised as excellent in the first place.
When it comes to female teachers, the paper highlights a substantial disparity compared with male colleagues in the perception of intelligence and scholarship. That gap may reflect the way roles associated with women in higher education are undervalued relative to roles associated with men. The pattern is not unique to academia, but higher education is not insulated from it.
The Shifting Standards Theory (SST) proposes that individuals can be evaluated against different standards depending on the stereotypes associated with the group an individual belongs to.
In practice, SST means the bar can move. Someone from a group stereotyped as lacking a trait may need less evidence to be seen as meeting a minimum standard, but much more evidence to be seen as clearly possessing that trait. Someone from a group stereotyped as strong in that area can face the reverse, because competence is assumed and only standout performance gets noticed.
Estimates of highest educational attainment are an example of a common-rule measure, where raters are asked to refer to an objective scale when making evaluations. Common-rule measures can produce assimilative effects. More subjective measures may produce either null or contrastive effects, which can mask the stereotyped view rather than remove it.
Minimum standards are the threshold at which there is suspicion that a target possesses a characteristic, such as being humorous. Confirmatory standards are the point at which there is certainty. People stereotyped as deficient in x can find it harder to convince raters that they genuinely possess x, because confirmatory standards rise.
- Minimum standards for competent performance of x-related tasks are lower for a group stereotyped as incapable of x.
- Minimum standards for competent performance of x-related tasks are higher for a group stereotyped as proficient in x, and are in fact very similar to the confirmatory standards.
People are more likely to assimilate to stereotypes when they are making zero-sum judgements. Non-zero-sum decisions do not require taking anything away, and they show more null and contrastive effects.
Male students are significantly less likely than female students to regard female teachers as excellent. Even without comparing nominations for male teachers, nominations by female students were overrepresented. The low frequency of male-student to female-teacher nominations matches wider evidence that female teachers are often evaluated less favourably, particularly by male students.
The unequal gender distribution of nominations suggests that gender can influence whether teaching excellence is recognised at all, and it raises adjacent questions about whose voices are missing from evaluation data. The gender distributions for the themes of availability and supportiveness were also more unequal than expected. Male students mentioned these themes less frequently than expected when nominating male teachers, while female students mentioned them more frequently than expected when nominating female teachers. That matters because the same behaviour may be noticed, valued, or rewarded differently depending on who performs it.
Themes linked to stereotypically masculine traits were also overrepresented in female-student to female-teacher nominations. Female teachers who emphasised those traits were nominated more often. That suggests some women may receive more recognition when they are seen to display qualities that sit outside traditional gender expectations.
The results also show a slight female bias in the themes of approachable and engaging. This fits gendered expectations that women should be warm and nurturing in interactions with others. In other words, praise is not always neutral, it can still reflect the roles people expect women to fill.
An analysis of the effects of gender in student conceptualisations of teaching excellence reveals that there are gendered differences in whether teaching excellence is recognised in the first place and what it is recognised to be.
For universities, the practical takeaway is clear. If you rely on nominations, awards, or open comments to identify teaching excellence, you need to test whether the criteria are behaving differently across staff groups. Pairing evaluation text with structured analysis makes it easier to see when praise is reflecting stereotypes, not just performance.
It may be tempting to assume that there is objectivity in how teaching is evaluated, whether by students or other stakeholders; however, to ignore the role of sociocultural norms and their inextricability from standards and benchmarks would leave unchallenged a hegemony where those devalued in society are also devalued in HE.
FAQ
Q: How does the cultural and ethnic background of students and teachers influence the perception of teaching excellence?
A: Cultural and ethnic background can shape what students notice, how they read authority or warmth, and which teaching behaviours they view as excellent. That means the same classroom practice may be interpreted differently across student groups. Incorporating Student Voice initiatives and analysing open comments alongside ratings can help institutions identify these differences and build a more inclusive view of teaching excellence.
Q: What methods were used to analyse the free-text data from student nominations, and how were biases identified and quantified?
A: The original post does not set out the full analytical method, but this kind of work usually combines close qualitative coding with text analysis methods used in higher education such as keyword extraction, theme clustering, and sentiment analysis for UK universities. Bias can then be examined by comparing who gets nominated, which traits are mentioned, and how those patterns vary by student and teacher gender. The value of this approach is that it surfaces systematic bias that raw nomination counts can hide.
Q: What interventions or strategies are being considered or have been implemented to address the biases identified in the study?
A: Useful responses include reviewing award criteria, widening the definition of teaching excellence, and training students and staff to recognise how bias can affect nominations. Universities can also check whether praise and criticism are distributed unevenly across staff groups by combining survey scores with open-text analysis. The goal is not to discount student feedback, but to interpret it more fairly and act on it with better safeguards.
References
[Paper Source]: Kathryna Kwok and Jacqueline Potter "Gender stereotyping in student perceptions of teaching excellence: applying the shifting standards theory"
DOI: 10.1080/07294360.2021.2014411
Request a walkthrough
Book a free Student Voice Analytics demo
See all-comment coverage, sector benchmarks, and reporting designed for OfS quality and NSS requirements.
- All-comment coverage with HE-tuned taxonomy and sentiment.
- Versioned outputs with TEF-ready reporting.
- Benchmarks and BI-ready exports for boards and Senate.
UK-hosted · No public LLM APIs · Same-day turnaround
Related Entries
- Halo Effects in the Student Voice: Unwanted Correlations
- Module Evaluation, Likability and The Case For Free-Text Comments
- Revolutionising Student Evaluation Data Use in UK Higher Education
- Student voices in evaluation - motivations and perceptions
- Navigating the storm - the impact of student voice on UK academics