AN INVESTIGATION INTO THE CHALLENGES FACING ELLS IN ONTARIO’S MULTILINGUAL ENGLISH LANGUAGE ARTS CLASSROOMS
Bibliographic record
Abstract
Given the multicultural, multilingual nature of classrooms in Ontario, English language learners (ELLs) experience challenges while studying English-medium literacy material in mainstream English Language Arts (ELA) classrooms. The purpose of this qualitative case study was to observe the reading comprehension performances of ELLs, given the objectives of English elementary curriculum (EEC) (Ministry of Education, Ontario/ MEO, 2006). Students from grades 5 and 6 in a public school in southwestern Ontario participated in the study. The researcher investigated the challenges three ELLs experienced when faced with English medium literacy material and the reasons for these challenges. Also, the application of different learning strategies in mainstream ELA classrooms was investigated.\nQuestionnaires, observations, and interviews were used to collect data. A combination of two approaches, case study descriptions and cross-case analysis, Was used to analyze the data. The findings suggest that ELLs face some challenges in comprehending some topics and ideas presented in English-literacy material. The negative approach of ELLs towards learning activities and unfamiliar cultural backgrounds of English-literacy material are among the major challenges. Thus, their parents and teachers need to help ELLs learn to use suitable learning strategies to overcome these challenges. Finally, this research suggests that it is useful to examine the experiences of ELA teachers and ELLs in order to identify and improve these students’ English reading comprehension achievements.
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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 | 0.002 | 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 | 0.014 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".