The Influence of Partners’ L2 Proficiency on Test-Takers’ Performance in Paired Oral Assessment
Bibliographic record
Abstract
This study is intended to explore the influence of partners’ L2 proficiency on test-takers’ performance and on their interaction patterns in paired oral assessment, and it tries to analyze the underlying causes on the basis of Vygotsky’s theory of the zone of proximal development (ZPD) and Storch’s patterns of interaction. An experiment including three rounds of tests was carried out with 12 students who were selected out of 60 from a top university in China. The findings showed that the high-level test-takers received the highest scores when paired with middle-level partners; the middle-level test-takers scored the highest and the lowest when paired with high-level partners and low-level partners respectively; the low-level test-takers had basically the same scores when paired with those of the other two different levels. The results could be partly explained by the fact that the higher level partners stimulated the lower level test-takers to the upper limit of ZPD through language interaction, but the mechanism of ZPD might not work when partners’ proficiency levels were much higher than the ceiling of test-takers’ ZPD. Besides, test-takers’ performance was mediated by pair interaction patterns. The implications of the study not only deliver suggestions for fairness of paired oral assessment but also provide alternatives for test-takers to achieve higher scores in a paired speaking test by choosing partners of different L2 proficiency levels.
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".