Exploring the Emotional Responses of Undergraduate Students to Assessment Feedback: Implications for Instructors
Bibliographic record
Abstract
Summative assessments tend to be viewed as high-stakes episodes by students, directly exposing their capabilities as learners. As such, receiving feedback is likely to evoke a variety of emotions that may interact with cognitive engagement and hence the ability to learn. Our research investigated the emotions experienced by undergraduate students in relation to assessment feedback, exploring if these emotions informed their learning attitudes and behaviours. Respondents were drawn from different years of study and subject/major. A qualitative approach was adopted, using small group, semi-structured interviews and reflective diaries. Data were analysed thematically and they revealed that receiving feedback was inherently emotional for students, permeating their wider learning experience positively and negatively. Many students struggled to receive and act upon negative feedback, especially in early years, when it was often taken personally and linked to a sense of failure. Negative emotional responses tended to reduce students’ motivation, self-confidence, and self-esteem. Some students, especially in later years of study, demonstrated resilience and engagement in response to negative feedback. By contrast, positive feedback evoked intense but fleeting emotions. Positive feedback made students feel cared about, validating their self-worth and increasing their confidence, but it was not always motivational. The paper concludes with recommendations for instructors, highlighting a need to communicate feedback carefully and to develop student and staff feedback literacies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".