An Empowerment Tool for Teaching English Effectively to Refugees: A Case Study of Syrian Adult Refugees in the UK
Bibliographic record
Abstract
There is a growing demand today to fill the gap in the literature with studies that focus on teaching English to adult refugees who are illiterate or have had interrupted education and no English proficiency. This group has been ignored because ESOL courses are not designed to serve their needs, namely, to be self-reliant and socially integrated. This paper shares my personal experience with six Syrian adult refugees, 2 females and 4 males ranging in age from 26 to 52, with either interrupted elementary education or illiteracy. They had no English language proficiency and could neither speak nor understand English at all. Their first language was Arabic. The rote learning approach was used as an empowerment tool to teach self-reliance in speaking and listening when dealing with these participants’ priority themes. The approach is based on memorization using both repetition and recall. Their remarks of progress towards self-reliance varied due to age and motivational factors. Four participants moved from A0 level to A1+, while the other two reached A2 level. Gaining self-reliance helped them to socially integrate, find a job, and gain greater self-confidence. Results of the study might be useful to teachers who are involved in teaching English to refugees as volunteers and to refugees’ organizations to shift from relying totally on interpretation to rote learning when specifically dealing with this group of refugees.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".