The Use of Pre‐/Posttest and Self‐Assessment Tools in a French Pronunciation Course
Bibliographic record
Abstract
Abstract This study investigated the relationships between students' self‐assessments and experts' assessments in a university French pronunciation course for nonnative speakers using a pre‐/posttest design. Results indicated that students were relatively accurate when making a global assessment (Time 1) and when judging some specific aspects of their French pronunciation (Time 2), although they tended to overestimate the extent to which their abilities were native‐like. Their self‐assessments were most accurate when evaluating linguistic components for which they had learned concrete rules (e.g., liaisons). In addition, data revealed that students became more native‐like in their pronunciation, particularly with regard to nasal and other new vowel sounds, and a content analysis of students' responses to a free‐response self‐analysis query at the end of the course indicated that their awareness of their pronunciation difficulties had increased. Taken together, the study found that self‐assessment may be a valuable pedagogical tool for helping second language learners to acquire more authentic 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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".