Emotions Experienced by Instructors Delivering Written Feedback and Dialogic Feed-Forward
Bibliographic record
Abstract
Understanding the emotions experienced by higher education instructors related to assessment feedback, how instructors understand student emotions, and how instructors might manage these emotions positively, can help to secure the educational benefits of feedback. In this research, we aimed to explore the emotional responses that instructors experienced through the giving and receiving of assessment feedback. We undertook qualitative data collection, carrying out individual semi-structured interviews with instructors from three universities who had administered a dialogic feed-forward intervention on one of their teaching units. The full interview transcripts were analysed inductively using thematic analysis. Five main themes emerged from the interview data: 1. Summative written feedback aroused largely negative emotions in instructors because they felt distanced from their students; 2. Instructors experienced a broad range of emotions related to dialogic feed-forward encounters, emerging from their proximity to students; 3. Dialogic feed-forward, as an affective encounter, was emotionally challenging for instructors; 4. Dialogic feed-forward built strong learning relationships between students and instructors, strengthening students’ sense of belonging; 5. Dialogic feed-forward was transformational for instructors as educators. We consider the implications of our findings for instructors and wider assessment and feedback practices, including emotional labour, promotional reward, and instructor professional development.
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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 | 0.007 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".