A Generalized Framework for Describing Question Randomization
Bibliographic record
Abstract
The rise of online assessments has motivated the development of randomized question banks, with randomization referring to the generation of different variants of a question. Although not all randomization efforts are equally effective in generating question isomorphs, the current classification of questions solely as randomized or not fails to address the varying degrees of randomization. To address this limitation in describing the diversity of randomization designs, we introduce a framework that outlines six distinct randomization levels. Additionally, we designed practical guides to assist educators in effectively using the framework, aligning with their pedagogical objectives. Through our application of this framework to classify around 200 questions from two courses, we further highlight the generalizability of the framework and reveal insights into the considerations and challenges associated with incorporating question randomization into computer science curricula.
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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 | 0.122 | 0.182 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".