Les manuels d’écriture sont-ils des vecteurs motivationnels au niveau universitaire?
Bibliographic record
Abstract
La motivation à l’écrit en langue seconde pose souvent des défis. Cette étude empirique de type qualitative avait pour objectif de voir si les manuels d’écriture ou de composition étaient des vecteurs motivationnels pour des étudiants universitaires inscrits en français aux niveaux intermédiaire et avancé. À partir de critères motivationnels identifiés par la recherche (Dörnyei, en particulier), nous avons analysé les approches pédagogiques utilisées dans des manuels sélectionnés ; ensuite nous avons examiné les activités proposées par ces manuels et enfin nous avons fait une enquête auprès de professeurs et d’étudiants utilisant ces même manuels. Les résultats de l’analyse et de l’enquête ont fait ressortir certaines constantes dans la perception des apprenants et les enseignants en ce qui concerne l’écrit et ses processus. Ils ont aussi mis en évidence que l’intégration systématique, dans de nouveaux manuels ou programmes, des critères motivationnels identifiés, pourrait stimuler l’intérêt de l’écrit auprès des apprenants. Abstract: Motivation to write in a second language often poses challenges. This empirical, qualitative study evaluates whether writing or composition textbooks were motivational factors for university students enrolled in French at intermediate and advanced levels. Using motivational criteria identified by research (Dörnyei in particular), we have analysed the pedagogic approaches used in selected textbooks; we then examined the activities they proposed and finally we conducted a survey of professors and students who used the textbooks. The results of the analysis and survey showed certain constants in the perception of writing and the writing process by students and teachers. They also highlight that systematic integration of identified motivational criteria in new textbooks or programs could stimulate students’ interest in writing. Article reçu le 2011-09-09; accepté le 2012-01-23
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".