Using Collaborative Strategic Reading with Refugee English Language Learners in an Academic Bridging Program
Bibliographic record
Abstract
Refugee students arrive in Canada with varying amounts of previous formal education. School-aged refugees who lack a solid first language education may find learning to read in English and studying subject content especially challenging. If these students leave school, they depart with inadequate English reading proficiency for further academics or job training. Reading strategy instruction could potentially contribute to improving their reading comprehension. In this article, I outline the implementation of Collaborative Strategic Reading in an academic bridging program for low-literate refugee students 17 to 25 years old, followed by some of the observed accompanying benefis. Descriptions and examples of activities used are included, along with references for additional teaching resources. Les étudiants réfugiés arrivent au Canada avec des niveaux de scolarité formelle variables. Les réfugiés en âge d’être scolarisés qui n’ont pas une bonne base scolaire dans leur première langue risquent d’avoir du mal à apprendre la lecture et les matières académiques en anglais. Si ces élèves qui ent l’école, ils partent sans la compétence en lecture de l’anglais nécessaire pour poursuivre leur scolarité ou une formation professionnelle. L’enseignement de stratégies de lecture pourrait contribuer à l’amélioration de leur compréhension en lecture. Dans cet article, je retrace la mise en œuvre d’un programme de lecture stratégique collaborative dans un programme de transition académique auprès d’élèves réfugiés peu alphabétisés et âgés entre 17 et 25 ans, et j’évoque quelques uns des bienfaits qui en découlent. Des descriptions et des exemples d’activités sont fournis, ainsi que des références indiquant des ressources pédagogiques supplémentaires.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".