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Record W7010844713

L’apprentissage adulte à travers la ludicisation dans un contexte d’éducation aux adultes

2025· other· fr· W7010844713 on OpenAlexaboutno aff

Bibliographic record

VenueConstellation (Université du Québec à Chicoutimi) · 2025
Typeother
Languagefr
FieldChemistry
TopicChemical Reactions and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Research methodologyStatistical analysis
DOInot available

Abstract

fetched live from OpenAlex

Notre recherche s’intéresse à l’apprentissage-enseignement de l’anglais langue seconde (ALS) par le jeu dans la formation des adultes. La ludicisation de la grammaire anglaise en classe adulte permet, entre autres, de réviser de la grammaire, de développer la pratique orale, de se préparer aux examens de fin de session. Bien que des recherches antérieures aient montré que l’apprentissage à travers le jeu s’avère bénéfique dans certains contextes, notamment pour les enfants, pour ce qui est de l’utilisation des jeux en contexte adulte pour l’apprentissage intentionnel de la grammaire, les recherches arrivent à des résultats contradictoires. Afin d’y voir plus sur l’impact des bénéfices des jeux dans l’apprentissage de l’anglais en formation des adultes, nous avons mené une recherche collaborative avec six enseignantes en anglais langue seconde qui ont essayé des jeux crées pour cette recherche dans leurs classes dans un centre de langue privé du Saguenay-Lac-Saint-Jean. Au moyen de deux questionnaires (pour les étudiants) et de deux entrevues (pour les enseignantes), et en nous appuyant sur l’analyse thématique (Paillé et Mucchielli, 2021), nous avons documenté les perspectives de ces enseignantes et de quinze de leurs étudiants sur deux jeux élaborés dans le cadre de cette étude et testés en classes. Les analyses des trente (30) questionnaires des étudiants et des douze (12) entrevues des enseignantes démontrent que le jeu et la ludification d’éléments précis de grammaire sont des approches didactiques porteuses pour l’enseignement de l’anglais en contexte de classe avec des adultes. À cet égard, les étudiants adultes de notre étude ainsi que leurs enseignantes ont révélé des opinions positives, des perspectives intéressantes sur le jeu et son usage dans la formation générale des adultes. This research focuses on the act of learning-teaching of English as a second language (ESL) through games in adult education. Gamification of English grammar in adult classes allows students to review grammar, develop oral communication, and prepares students for end-of-session exams, amongst other things. When it comes to using games in adult education to learn grammar, much of the previous research done shows contradictory results, even if previous research for younger leaners has shown that learning through games can be beneficial in certain contexts. To identify the impacts and benefits of games in English learning in adult education, this research used a collaborative approach with the help of six English second language teachers who have tested games created for this research in their respective classes at a private school in Saguenay-Lac-St-Jean. Two questionnaires (for the students) and two interviews (for the teachers) were used to document their respective perspectives. This research used a thematic analysis (analyse thématique) (Paillé et Mucchielli, 2021) and documented the perspectives of these teachers and fifteen of their students on games made for this research and tested in class. Thirty (30) questionnaires and twelve (12) interviews were analysed. They showed that games, and gamifying precise grammar elements, can create multipurpose tools that can be promising in terms of educational approaches in adult education. Using of the data collected with the questionnaire and interviews, students and teachers revealed their positive opinions and positive perceptions of games and of their use in adult education.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.778
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.2870.000

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.

Opus teacher head0.009
GPT teacher head0.205
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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