Émotions et apprentissage de l'anglais dans l’enseignement supérieur : une approche visuelle
Bibliographic record
Abstract
Dans le contexte actuel, où l’anglais prend une place de plus en plus importante dans l’enseignement à l’université, aussi bien pour la recherche que pour les enseignements, il est important d’explorer les attitudes des acteurs se voyant utiliser l’anglais de manière régulière au sein de l’enseignement supérieur en France. Cet article propose des outils à la fois pédagogiques et analytiques pour réfléchir sur les vécus d’enseignants-chercheurs et d’étudiants qui utilisent et apprennent l’anglais à la Faculté des Sciences de Nantes, France. Des outils méthodologiques qui associent le langage au dessin, sont proposés sous formes de mind-maps du cerveau (Buzan, Reynolds), de graphiques circulaires, de portraits corporels du langage (Busch) et de parcours de l’apprenant de l’anglais (Kehrwald). Les créations visuelles sont analysées comme étant des exemples d’identités non-figées, créées par des locuteurs qui réagissent émotionnellement à leurs environnements d’apprenants en tant que créateurs de l’anglais (Jenkins). Emotions and learning English in higher education : a visual approach. Abstract: In France, where English is gaining ground in higher education, it is important to explore the attitudes of those who now use it on a regular basis. This article describes the pedagogical and analytical tools used to gather learner-identity accounts from academics and postgraduate student users of English at the university of Nantes, France. The methodological approaches, which combined language and drawing, were based on mind-maps (Buzan, Reynolds), pie-charts, language portraits (Busch), and language learner histories (Kehrwald). The resultant notes and drawings were analysed as representations of non-fixed identities of rightful creators of English who reacted emotionally to their learning environments (Jenkins).
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.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".