Teachers' Perceptions of French Immersion Inclusionary Strategies
Bibliographic record
Abstract
Historically, students with diverse learning needs were segregated from mainstream classrooms. It was not until the 1990’s that this model was replaced with inclusive education, whereby all students are well integrated into the mainstream classroom and all students learn together with some modifications that suit their needs. French immersion (FI) programs in Canada aim to provide non-francophone students with the opportunity to acquire fluency in speaking, reading, writing, listening and communicating in French by the end of Grade 12 by immersing them in an almost all-French instructed education (Calgary Board of Education, n.d.). Despite the large body of research exploring inclusion and its manifold benefits for all students in mainstream programs (Avramidis et al., 2000; Dupoux, Wolman, & Estrada, 2005; Subban & Sharm, 2006; Mashburn et al., 2008; Baker et al., 1994), FI inclusion still remains notably underexplored, especially the ways teachers use strategies to achieve an inclusive classroom (see Arnett and Bourgoin, 2018, for an exception). The goal of this study is to gain a better understanding of FI teachers’ perspectives on the current inclusionary strategies that are used to encourage students to reach their full potential and remain in FI for high school. Surveys were administered to grade 9 teachers, principals, and vice-principals in FI programs across Calgary school boards. Teachers additionally completed a Teacher Attribution Scale (Ghanizadeh & Ghonsooly, 2015) to identify the factors they believe are responsible for the successful implementation of inclusion in the classroom. Using a mixed-methods approach, this study confirms previous research findings that suggest students are often likely to leave the program if they are encountering academic difficulty. This study suggests that teachers’ attempts to try to address these difficulties through inclusionary practices are hindered by the lack of preparation time, large class sizes, or not having sufficient knowledge on inclusionary strategies. This study reveals that teachers often do not hold themselves ultimately responsible in ensuring that students find success in FI, but rather believe that it is the students’ and parents’ responsibility.
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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 | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".