La motivation : un facteur qui peut déclencher la parole. La motivation et son influence sur l’expression orale en interaction en cours d’anglais
Bibliographic record
Abstract
This thesis is based on the theoretical context that motivation is at the heart of how to inspire students to participate more actively and autonomously in their learning, especially in English language classes. The aim is to prove that by changing certain points within my teaching, such as putting them into smaller groups to work, the students feel more responsible, independent and confident in their oral participation. As a teacher I aim to intervene less in class and to let them decide on how to respond to the topics. I hope to increase their spontaneous interaction as well as their motivation towards their learning in general. A special series of classes was set up for a class of thirteen year olds in which they were to hold a series of debate-like activities on a variety of subjects linked to their interests. The final task is for them to record themselves to share their ideas with their Canadian penpals. The students were certainly more stimulated by the more open aspects of the new classes and by having the chance to talk more in class, but still expressed their awareness of certain limits and of the need to have the teacher nearby as a helper.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".