Adaptive Comparative Judgment: A Tool to Support Students' Assessment Literacy
Bibliographic record
Abstract
Comparative judgment in assessment is a process whereby repeated comparison of two items (e.g., assessment answers) can allow an accurate ranking of all the submissions to be achieved. In adaptive comparative judgment (ACJ), technology is used to automate the process and present pairs of pieces of work over iterative cycles. An online ACJ system was used to present students with work prepared by a previous cohort at the same stage of their studies. Objective marks given to the work by experienced faculty were compared to the rankings given to the work by a cohort of veterinary students (n=154). Each student was required to review and judge 20 answers provided by the previous cohort to a free-text short answer question. The time that students spent on the judgment tasks was recorded, and students were asked to reflect on their experiences after engaging with the task. There was a strong positive correlation between student ranking and faculty marking. A weak positive correlation was found between the time students spent on the judgments and their performance on the part of their own examination that contained questions in the same format. Slightly less than half of the students agreed that the exercise was a good use of their time, but 78% agreed that they had learned from the process. Qualitative data highlighted different levels of benefit from the simplest aspect of learning more about the topic to an appreciation of the more generic lessons to be learned.
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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 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".