Bridging the Gap between English Language Learners and Native English Speaking Students in Science Education
Bibliographic record
Abstract
Ontario welcomes up to 100,000 immigrants every year. One in four students in Ontario schools are born outside of Canada. Research shows that the population of learners from diverse and non-English speaking backgrounds in the Ontario classroom is continuously growing (the Ontario Ministry of Education, 2007). The aim of this qualitative research project is to use a literature review and semi-structured interviews to explore how mainstream science teachers respond to the needs of English Language Learners (ELLs) in their classes, while reducing the already-present achievement gap observed between ELLs and their fluent English-speaking peers. This study also examines the varying instructional strategies used by mainstream science teachers to overcome any obstacles that arise in terms of delivering effective scientific teachings to ELLs. The main question guiding this research is: “How do mainstream classroom science teachers facilitate the learning of the English language along with the disciplinary knowledge in science for their ELLs?” Data was collected through semi-structured interviews with three science teachers/educators from Toronto schools. Findings showed how these teachers/educators are attempting to integrate ELLs into their science classes, and spoke to the varying challenges they face in terms of assessment, as well as the outcomes observed of ELLs. The implications of these findings suggest that more needs to be done in order to support pre-service and in-service teachers who teach science to ELLs, along with the integration of more effective support systems by ministries of education and school boards.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".