Applying Sadler’s principles in holistic assessment design: a retrospective account
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Holistic assessment is an evaluative approach in which assessors work backwards from an overall appraisal of work to determine the criteria relevant to individual student responses. One of the strongest proponents of this approach in higher education is Royce Sadler, whose theoretical contributions over recent decades provide a strong conceptual rationale for holistic assessment. While Sadler’s contributions are well renowned, few explicit accounts are available of Sadlerian theory in practice. The purpose of this reflective paper, as such, is to explain how we have attempted to synthesise and apply a Sadlerian theory of holistic assessment design in one of our own courses. Our intended contribution is to provide a relatively concrete interpretation of holistic assessment design which may serve as a point of reference for assessment design in higher education coursework.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it