A Methodology for Generating Items in Three or More Languages Using Automated Processes
Bibliographic record
Abstract
Educational and psychological tests are administered to examinees in different languages across different cultures throughout the world. The challenges inherent to translating and adapting multilingual and multicultural assessment are enormous. The purpose of this paper is to describe and illustrate a new methodology that can be used to generate items in multiple languages. The method is presented as a three-stage process where, first, context translation begins when the context of the model required for item generation is translated or adapted appropriately for each language group; second, words and key phrases are translated; third, content assembly occurs where computer algorithms place the words and key phrases into the context-specific item model. Then, we demonstrate how the method can be applied to a diverse sample of item models in math and science to generate thousands of multilingual test items. Finally, results are presented from a substantive review designed to evaluate item quality which revealed that 91% of the generated items were judged to be acceptable by two bilingual test development specialists. Directions for future research are also presented.
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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 | 0.020 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".