Module Evaluation, Likability and The Case For Free-Text Comments
Updated Apr 23, 2026
If module evaluation scores mostly reflect whether students like an instructor, they are a weak proxy for teaching quality. This study suggests perceived likability may explain a large share of those scores, which is why free-text comments matter when you want evidence you can actually use to improve teaching.
This study examined how instructor likability shapes student evaluations of teaching. It found that, even before you account for teaching practice or course design, perceived likability explained around two-thirds of the total variance in evaluation scores.
Module evaluations are usually built on the assumption that students are rating teaching effectiveness. In practice, students often answer through the lens of an overall impression or concern, a pattern that overlaps with halo effects in the student voice, rather than the specific question in front of them.
- Many module evaluation instruments do not measure instructional effectiveness as cleanly as intended.
- Students do not always evaluate teaching in a genuinely multidimensional way.
- A student's early impression of an instructor can remain strongly related to the score they give after a year of interaction.
A practical implication is that module evaluations do not reliably improve teaching, even when institutions judge improvement using the same instruments. If the purpose of evaluation is unclear, students may respond in ways that reflect bias, impression, or satisfaction rather than teaching quality, which echoes wider concerns about how reliable student evaluations of teaching actually are.
The statistical analyses also suggest that these survey instruments have little relationship with measurable learning. For teams responsible for quality enhancement, that matters: a neat score can still hide a weak connection to the outcome you actually care about.
The study further found that perceived instructor likability was a strong predictor of self-reported learning and grades. There are several possible explanations for that relationship, but the research does not yet isolate a single cause.
Researchers also found that students' ratings of instructor likability, personality, and module evaluation responses were closely linked. That has led some scholars to argue that many evaluation instruments function more like likability scales than measures of teaching effectiveness.
This study therefore shows that instructor likability is not a simple, standalone trait. It is made up of several interrelated factors, and not all of them map neatly to teaching quality.
Seen this way, student evaluation of teaching starts to resemble a customer satisfaction exercise: it captures what students liked and disliked, but not necessarily how effective the teaching was.
That is where free-text comments become far more useful. Open comments give institutions a more nuanced view of what students experienced, and when analysed well they can turn broad impressions into specific, actionable teaching insight.
FAQ
Q: How can educational institutions effectively analyse free-text comments to extract actionable insights?
A: Educational institutions can analyse free-text comments effectively by combining structured text analysis tools for education with human review. Good tools surface recurring themes, sentiment, and patterns across large volumes of feedback, which helps teams move beyond anecdote and see where action is needed. Comments can then be grouped around issues such as teaching practice, assessment, or course organisation. Staff review still matters because context determines whether a pattern reflects a real teaching issue or a one-off frustration.
Q: What are the specific challenges associated with interpreting free-text feedback from students, and how can these be addressed?
A: Free-text feedback is harder to interpret because students vary in how specific they are, often use ambiguous language, and can bundle several issues into one comment. A combined approach works best: structured text analysis handles scale and consistency, while human review adds context and nuance. Clear coding frameworks, shared definitions, and staff training also reduce the risk of over-interpreting isolated comments or reading bias into the data. This helps institutions turn comments into evidence rather than opinion.
Q: How can instructors use the insights gained from free-text comments to improve their teaching methods and student learning outcomes?
A: Instructors can use free-text insights to pinpoint what students are actually experiencing, not just how they scored a module. Repeated comments about unclear explanations, assessment pressure, or weak feedback loops show where teaching changes may have the biggest effect. Staff can then respond with targeted adjustments, such as clearer examples, more structured revision support, or changes to how feedback is delivered. Closing the loop with students also shows that their comments are taken seriously, which can improve engagement and future response quality.
References
[Paper Source]: Dennis Clayson (2022) The student evaluation of teaching and likability: what the evaluations actually measure, Assessment & Evaluation in Higher Education, 47:2, 313-326,
DOI: 10.1080/02602938.2021.1909702
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