Educational technology to train teachers of minority languages in Canada
Bibliographic record
Abstract
Canada is the world’s second largest country by total area, occupying most of northern North America. For its ten provinces and three territories, Canada must offer education in either English or French, the country’s two official languages. This raises many challenges, particularly in areas or provinces where one language, usually French, is a minority language. For example, in Ontario, Canada’s second largest province, there is a significant shortage of qualified French-speaking teachers. Moreover, although many schools employ some teachers who are not fully qualified, it would be unthinkable to remove them from the classroom for further training, given the lack of teachers. To cope with this challenge, the School of Education of Laurentian University launched a distance teacher training program. Early into the program, the candidates found that distance education was insufficient to help them meet the challenges of classroom teaching. After conducting interviews with our prospective teachers (n = 125), we realised that the theoretical content provided through the distance program needed to be complemented by classroom observations. However, this appeared to be impossible in the circumstances. In this paper, we highlight the findings of our study on the use of educational technologies to train minority-language teachers in Canada. We will focus specifically on the video component that we included in the distance training course. We will show how the videos (over 75 real-life recordings of teachers and pupils in a variety of common pedagogical situations) actually benefited the teachers-in- training, who reported increased feelings of competence, among others.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 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) | 0.003 | 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".