“Just Engage in It or Not, You Get Out What You Put In”: Student and Staff Experiences of Feedback and Feedforward in Workplace-Based Learning Environments
Bibliographic record
Abstract
Feedback is central to student learning in the veterinary workplace. Feedforward, a related concept, is used to describe the way information about a student's performance may be used to improve their future performance. Feedback and feedforward practices are diverse, with varied student and staff understandings of the nature and purpose of feedback (feedback literacy). This study compared the practices of feedback and feedforward in a range of programs in one institution during student transitions from the classroom to workplace-based learning environments. The study adopted a broad inter-professional approach to include health care programs and social work and theater and performance studies. Profession-specific focus groups were conducted with contribution from 28 students and 31 staff from five different professions. Thematic analysis revealed that students and staff shared an understanding of the feedback and feedforward concepts, and both groups recognized the importance of emotional and relational aspects of the process. Students and staff across all professions recognized the impact of time constraints on the feedback process, although this was particularly highlighted in the health science professions. Social work and theater and performance studies students demonstrated a more nuanced understanding of the emotional and relational aspects of feedback and feedforward. Overall, the approach highlights similarities and differences in practices and experiences in different workplace contexts, creating opportunities for cross-disciplinary learning, which may have relevance more widely in higher education programs with workplace-based elements. The study underpinned the development of the LeapForward feedback training resource (https://bilt.online/the-leapforward-project/).
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".