Expert validation of fit-for-purpose guidelines for designing programmes of assessment
Bibliographic record
Abstract
BACKGROUND: An assessment programme, a purposeful mix of assessment activities, is necessary to achieve a complete picture of assessee competence. High quality assessment programmes exist, however, design requirements for such programmes are still unclear. We developed guidelines for design based on an earlier developed framework which identified areas to be covered. A fitness-for-purpose approach defining quality was adopted to develop and validate guidelines. METHODS: First, in a brainstorm, ideas were generated, followed by structured interviews with 9 international assessment experts. Then, guidelines were fine-tuned through analysis of the interviews. Finally, validation was based on expert consensus via member checking. RESULTS: In total 72 guidelines were developed and in this paper the most salient guidelines are discussed. The guidelines are related and grouped per layer of the framework. Some guidelines were so generic that these are applicable in any design consideration. These are: the principle of proportionality, rationales should underpin each decisions, and requirement of expertise. Logically, many guidelines focus on practical aspects of assessment. Some guidelines were found to be clear and concrete, others were less straightforward and were phrased more as issues for contemplation. CONCLUSIONS: The set of guidelines is comprehensive and not bound to a specific context or educational approach. From the fitness-for-purpose principle, guidelines are eclectic, requiring expertise judgement to use them appropriately in different contexts. Further validation studies to test practicality are required.
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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 | 0.274 | 0.491 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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".