Teaching FSL with AIM? An elementary school case study. Studies by Undergraduate Researchers at Guelph
Bibliographic record
Abstract
from Canadian secondary schools who have acquired acquired a functional knowledge of their second language. The goal set out by this publication has yet again heightened the polemic around the most effective way to learn a second language. Contributing to the corpus of instructional materials for the teaching of FSL in Canada, Wendy Maxwell, a French teacher in British Columbia, developed the AIM (Accelerative Integrated Method). The AIM proposes to accelerate the learning of the target language through the use of gestures (The Gesture Approach) so that students can understand and speak in the second language (SL) as early as possible. In spite of the growing popularity and favorable reception of the program by teachers, there is very little research examining its effectiveness in the classroom. This article proposes to add to the current body of research by examining the efficiency of the AIM for the teaching of FSL on a practical and theoretical level. Data acquired from a proficiency test administered to elementary core French students taught with the AIM will serve as a springboard in defining the potential outcomes one can attain with the program. Finally, a review of the literature on the AIM as well as the use of gesture in the SL classroom will bring into evidence the theoretical merits of the method. n 1982 Stern shed light on the poor state of core French programs in Canada and the need to reform them in the hope of producing students with a functional level of
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".