Assessment, feedback and the alchemy of learning
Bibliographic record
Abstract
CONTEXT: Models of sound assessment practices increasingly emphasise assessment's formative role. As a result, assessment must not only support sound judgements about learner competence, but also generate meaningful feedback to guide learning. Reconciling the tension between assessment's focus on judgement and decision making and feedback's focus on growth and development represents a critical challenge for researchers and educators. METHODS: We synthesise the literature related to this tension, framed around four trends in education research: (i) shifting perspectives on assessment; (ii) shifting perspectives on feedback; (iii) increasing attention on learners' perceptions of assessment and feedback, and (iv) increasing attention on the influence of culture on assessment and feedback. We describe factors that produce and sustain this tension. RESULTS: The lines between assessment and feedback frequently blur in medical education. Models of programmatic assessment deliberately use the same data for both purposes: low-stakes individual data points are used formatively, but then are added together to support summative judgements. However, the translation of theory to practice is not straightforward. Efforts to embed meaningful feedback in programmes of learning face a multitude of threats. Learners may perceive assessment with formative intent as summative, restricting their engagement with it as feedback, and thus diminishing its learning value. A learning culture focused on assessment may limit learners' sense of safety to explore, to experiment, and sometimes to fail. CONCLUSIONS: Successfully blending assessment and feedback demands clarity of purpose, support for learners, and a system and organisational commitment to a culture of improvement rather than a culture of performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.043 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".