FRENCH IMMERSION TEACHER AND STUDENT PERCEPTIONS ABOUT SCIENCE INSTRUCTION IN A SECOND LANGUAGE SETTING
Bibliographic record
Abstract
In Ontario, Canada, the vision for French Immersion (FI) education is grounded in the federal linguistic duality approach, which perceives knowledge of Canada's two official languages (French and English) as an important part of Canadian history as well as a notable asset in terms of student interaction and employability on an international spectrum (Ontario Ministry of Education, 2013).In Ontario, students enrolled in English language schools have the option to be taught academic subjects in both French and English.In response to requests for instructional support from FI Science teachers, representatives from the Council of Ontario Directors of Education (CODE) approached Laurentian University researchers to embark on a project that would contribute to building the capacity of teachers who teach Science in French in the Junior and Intermediate grades (7-10).The current study utilized a mixed methods approach to evaluate teacher perceptions about teaching science to second language learners, teacher beliefs about their own science teaching efficacy, as well as students' adaptive learning engagement in science.A total of 37 grade 7-10 FI teachers and their respective 324 students from across Ontario, Canada, participated in the project.Data were collected through telephone interviews, completing the Science Teaching Efficacy Belief Survey (for teachers) and the Student Adaptive Learning Engagement in Science Survey (for students).The findings show that most teacher participants generally felt that they taught science effectively, and were continually striving to find new and innovative ways to engage their students in science classes.However, these teachers faced challenges with respect to finding suitable science resources that are suitable for teaching science to second language learners.Findings from students were mixed in terms of their self-efficacy and self-regulation, for those who expressed a genuine interest in science, they were more likely to be confident in their ability to succeed in FI science classes.The paper present the findings of the current study and postulates on the potential impact of these self-perceptions from students and teachers in the FI science classrooms, as well as some suggestions for improving FI Science teaching and learning.
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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 | 0.002 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".