Engager les apprenants dans l’auto-évaluation de leurs productions écrites : le cas du portfolio d’erreurs
Bibliographic record
Abstract
Résumé Dans cet article sont examinés la pertinence et l’impact d’une rétroaction ciblée sur la forme linguistique lors d’un cours disciplinaire non linguistique (cours en éducation) ainsi qu'en cours de rédaction avancé, alors que la langue d’instruction, en l’occurrence le français, n’est pas nécessairement la langue première desdits étudiants. Le but de cette étude, longitudinale, est d’évaluer la mise en œuvre d’un portfolio permettant aux étudiants, de manière semi-autonome, de mieux gérer les erreurs qu’ils produisent à l’écrit. Les soixante-deux étudiants ayant pris part à l’étude ont suivi plusieurs étapes depuis l’identification des erreurs, la codification, la correction, le calcul de la fréquence des erreurs produites par type. Ils ont ensuite été amenés à rédiger un texte réflexif. Le portfolio d'erreurs constitue donc un outil pédagogique grâce auquel les étudiants sont encouragés à participer activement au traitement de leurs erreurs, sans que cela n’entrave ni leur motivation ni la transmission des contenus de cours. Les résultats montrent une amélioration substantielle de la correction linguistique des étudiants. Abstract In this article, we examine the relevance and the impact of providing feedback on language errors in content-based courses where the language of instruction (French) is not necessarily the student's first language. The purpose of this two-semester long study was to implement a portfolio of errors which would allow the students to manage their language errors in an education course and a dissertation course. This portfolio is used to engage and raise awareness among students of the errors that they make in their written production. Sixty-two students took part in the study, where they followed several stages of the portfolio, from the identification and codification of the errors to the correction and tabulation (of the frequency) of the errors. Students took control of their own progress, and they submitted a self-reflection paper on their progress using this portfolio. The portfolio of errors supplied the professor with a platform where the students were encouraged to be active participants in the correction of their language without hindering their language competence or the course content. The results show a significant and consistent improvement of the language quality in the written production of the students.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".