Interacting in pairs in a test of oral proficiency: Co-constructing a better performance
Bibliographic record
Abstract
This study, framed within sociocultural theory, examines the interaction of adult ESL test-takers in two tests of oral proficiency: one in which they interacted with an examiner (the individual format) and one in which they interacted with another student (the paired format). The data for the eight pairs in this study were drawn from a larger study comparing the two test formats in the context of high-stakes exit testing from an Academic Preparation Program at a large Canadian university. All of the test-takers participated in both test formats involving a discussion with comparable speaking prompts. The findings from the quantitative analyses show that overall the test-takers performed better in the paired format in that their scores were on average higher than when they interacted with an examiner. Qualitative analysis of the test-takers' speaking indicates that the differences in performance in the two test formats were more marked than the scores suggest. When test-takers interacted with other students in the paired test, the interaction was much more complex and revealed the co-construction of a more linguistically demanding performance than did the interaction between examiners and students. The paired testing format resulted in more interaction, negotiation of meaning, consideration of the interlocutor and more complex output. Among the implications for test theory and practice is the need to account for the joint construction of performance in a speaking test in both construct definitions and rating scales.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".