Increasing Student Enrolment in Core French Programs in Ontario
Bibliographic record
Abstract
Commitment to French language education in policy documents is not enough; it is time to identify key barriers and realize transformative possibilities in order to increase the number of graduating students who are confident and fluent in both of Canada’s official languages. With a focus on the new FSL curriculum, this study investigates Core French programming in Ontario. The primary objective of my research was to determine strategies that grade 9 Core French teachers use to motivate and inspire more students to continue pursuing French language education beyond the mandatory requirement. In order to learn how teachers are working to increase student enrolment in Core French, I have conducted a qualitative research study using purposeful sampling to interview 3 experienced Core French Educators working in the Toronto District School Board in Ontario. The semi-structured interviews revealed three findings that impact student enrolment in Core French. First, students must value what they are learning and it is important that they progress in their ability to communicate in French. Next, although the new FSL curriculum is making a positive difference in the classroom, Core French teacher still plays a bigger role than the curriculum itself when looking at future enrolment. Teachers must properly engage students in language learning and must adopt a student-centered pedagogy. Finally, Core French teachers may face several challenges, such as a lack of resources, institutional barriers, and limited elective space, that can decrease student enrolment. Several implications of these findings for In-service Teachers, Administration, School boards, and the Ministry of Education are analyzed in chapter 5. This research study is vital to improving French language education in Ontario.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".