Review of Feedback in Higher and Professional Education: Understanding it and doing it well.
Bibliographic record
Abstract
This useful book is a compilation of relatively short but well referenced articles on different aspects of feedback in higher and professional education.The book has a strongly international feel with contributors from a number of countries, in particular Australia and Canada, most of whom are from a Medical or Medical Educational background, with some notable contributors from Education.The book covers theoretical and practical issues and provides an interesting and fresh take on a subject which has become high on the agenda of many HEIs recently.The introductory chapter by David Boud and Elizabeth Molloy succinctly states some of the key issues with feedback.The authors emphasise that the suggestions in the book are not designed to take up more of busy academics' time, but rather to provide fresh perspectives, and the reader is motivated to explore further.The chapter takes a student-oriented perspective and looks at the whole feedback process: "For feedback to be effective, attention needs to be focused more on what occurs before the generation of comments and what occurs afterwards" and they advocate embedding feedback in the whole module design process, rather than considering it separately.The chapter lists a number of problems with feedback, for example staff and students' perception of what feedback is, the impact of feedback on students' performance, the time-consuming task of providing feedback and the judgemental aspects of feedback.Finally this chapter provides the authors' definition of feedback: ______________
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".