Learning to bridge language and content: Teachers' experiences during a professional development initiative on content-based instruction
Bibliographic record
Abstract
Research shows that students in content-based instruction (CBI) programs, whereby an additional language is learned through a subject such as history, often have difficulties with linguistic accuracy. Teachers in these programs are also not given the adequate training required to focus on language forms while teaching subject matter. This qualitative study explores eight social studies teachers' lived experiences during a yearlong joint professional development initiative between McGill University and the Eastern Townships School Board (ETSB), launched to support them as they designed curricular units with a language focus that were implemented in French in schools designated as English-speaking. Although the call for professional development for effective CBI is widespread, the phenomenological realities and voices of those such initiatives are meant for—CBI teachers themselves—remain scarce in the existing literature. Through questionnaires, interviews, and close observations during every stage of the teachers' involvement, findings uncovered six core constituents and essential emotions that defined their experiences: enthusiasm, enlightenment, confusion, collaboration, satisfaction, and finally reservation. Several recommendations for future professional development initiatives regarding CBI are suggested.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".