Incorporating Translanguaging in Language Assessment: The Case of a Test for University Professors
Bibliographic record
Abstract
In this article, we report on our development of a translanguaged French/English listening task as part of the revision of a test for professors in a bilingual Canadian university. The primary objective in revising the test was to more authentically represent the target language use domain, which regularly includes translanguaging. We describe the development process for this listening task based on a translanguaged department meeting. We outline the decisions made in operationalizing translanguaging in the source documents as well as in task instructions and responses. A priori test validation activities will also be presented which include stimulated reflections by test takers during task trialing. From these reflections, we attempted to determine the extent to which the translanguaged elements supported or otherwise affected the candidates’ test-taking experience. In addition, a survey was conducted with faculty deans and others who make employment decisions on the basis of these test scores. These decision makers were asked to comment on the competence needed in typical activities of professors in the course of their work (some of which explicitly include translanguaging). We conclude with a discussion of the challenges involved in developing assessment tasks that make explicit the value of dynamic bilingual practices.
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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 | 0.049 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".