Teachers’ Perspectives on How Stakeholders Can Ameliorate Students’ Attitudes towards Core French
Bibliographic record
Abstract
The education system in the province of Ontario provides multiple avenues through which students can learn French. All Ontario students are required to at least take Core French, which entails studying the language as a subject from Grade 4 until Grade 9. The purpose of this study is to learn from teachers’ perspectives how stakeholders, namely parents and society, teachers, school boards, and the government, can improve students’ attitudes towards Core French. This research paper includes a rigorous literature review of notable researchers in the field such as Sharon Lapkin and Scott Kissau. In addition, four experienced teachers were interviewed in order to collect data and report new findings. Some recommendations made as a result of the findings include: Parents’ active participation and collaboration with teachers; teachers speaking the target language in the classroom, focusing on the but communicatif (communicative goal), using cross-curricular methods, avoiding strictly grammar lessons, creating a safe classroom environment and fostering a personal and emotional connection with the language; school boards adjusting certain recruitment, funding, and scheduling policies; the government mandating that Core French begin earlier (currently, Grade 4) and continue until Grade 12. Lastly, opportunities for further study are identified based on the limitations and questions raised in this paper.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".