A PROFESSIONAL DEVELOPMENT WORKSHOP FOR MAINSTREAM CLASSROOM TEACHERS TO SUPPORT ENGLISH LANGUAGE LEARNERS (ELLS)
Bibliographic record
Abstract
As Canadian classrooms become more diversified, there is a rising need to train mainstream classroom \nteachers to provide adequate support to English language learners (ELLs). Despite the extensive previous \nresearch and publications on the support of ELLs, mainstream classroom teachers have not received \nsufficient training, support, or guidance for implementing various strategies and pedagogies on how to \neffectively support ELLs in mainstream classrooms (Brewer & McCabe, 2014). This project is an \nexploration of effective teaching pedagogies that can be used to guide mainstream classroom teachers in \ncreating an optimal learning environment for ELLs. A review of current academic literature was \nundertaken to identify the challenges faced by ELLs in Canadian mainstream classrooms and research \ninformed strategies that can provide assistance to mainstream classroom teachers in teaching their ELLs. \nTo best prepare the many mainstream teachers to meet the needs of ELLs, a workshop was created to \nenhance professional development opportunities for educators interacting with ELLs. The workshop seeks \nto encourage mainstream teachers to employ effective and informed teaching practices when providing \nsupport to ELLs. The workshop has been broken into a four-session series to be offered consecutively \nover two days in a way that professional learning builds and solidifies over the course of each session. \nThe workshop should be administered with the people who are not specialists, as a way of enhancing \naccessibility.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.010 |
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".