Epistemic Emotions in Learning: Using Qualitative Inquiry to Explore Implications for Veterinary Educators in Responding to Student Emotions in Their Classrooms
Bibliographic record
Abstract
Veterinary students frequently experience heightened emotions, which can stimulate or compromise learning. The impact of student emotions on educators, or the ways that educators can respond to these, is less well known. This has potential impacts for educators' own emotional responses and for educators' effectiveness in supporting learning. To better support educators in facilitating student learning, this study sought to further understand how students' epistemic emotions are experienced by educators. We explored the experiences of educators from three international veterinary schools, using iterative interpretive analysis of workshop discussions designed and implemented for the purpose of the study. Analysis revealed that veterinary educators experience a range of emotions in the course of teaching their students, arising from events, such as emotional topics or clinical situations; receipt of grades; and the experience of uncertainty, for example, in teaching methods or open-ended tasks. The educators' responses to these included feeling overwhelmed and anxious-wanting to help facilitate student learning but lacking the tools to do so. Consequently, educators felt unable to engage effectively with students, and learning was deactivated. This could occur even when students were interested and curious. Educators' responses were particularly challenged by time and assessment pressures (needing to remain on topic and teach to learning outcomes). Strategies for responding to student emotions and to support development of educator emotional intelligence have been generated. These include a need for institutional recognition of the time resources necessary for educators to reflectively learn from complex situations experienced in their classrooms.
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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 | 0.044 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| 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".