Effects of benchmarking and peer‐assessment on French learners' self‐assessments of accentedness, comprehensibility, and fluency
Bibliographic record
Abstract
Abstract This study examined the effect of benchmarking and peer‐assessment activities on second language (L2) French learners' self‐assessments of accentedness, comprehensibility, and fluency. The learners, who included 25 L2 French students enrolled in a 15‐week university‐level French course, recorded two oral presentations at the beginning and the end of the course and self‐assessed their accentedness, comprehensibility, and fluency four times. In addition to regular course instruction, the treatment group also engaged in benchmarking (discussing and applying pre‐established evaluation criteria) and peer‐assessment (evaluating peers' speaking performances). The students' self‐assessments were compared with ratings of accentedness, comprehensibility, and fluency by 10 native‐speaking French raters. At the end of the course (i.e., for the second oral presentation), the treatment group showed greater alignment in self‐assessment of comprehensibility than the comparison group, relative to the external raters' assessments. Results highlight the value of assessment‐focused activities targeting L2 learners' awareness of pronunciation.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".