The Use of Pre‐/Posttest and Self‐Assessment Tools in a French Pronunciation Course
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it