Study of Cultural Challenges Faced by the Arab Learners of English in the United States of America
Bibliographic record
Abstract
Learning English poses many challenges to ESL/ EFL learners. These challenges are often attributed to differences in two different languages and the two different teaching and learning environments. However, along with the language teaching and learning related difficulties, ESL/EFL learners from certain communities also face other challenges due to cultural differences. This is quite noticeable in relation to Arab learners travelling to western countries like USA, UK, Canada, and Australia. Arab learners have to face cultural racism and negativity while studying and living in these countries. The negative portrayal of Muslims, particularly of the Arabs, has stereotyped Muslims and Arabs into certain types which further complicate their problems. They face difficulties in expressing their cultural and religious needs and adjusting in completely new and different culture. Being such students in the United States of America and having faced this problem personally, we have attempted to offer an overview of these cultural challenges faced by the Arab learners who travel to the States to pursue their higher educations. It is expected that the findings of the study may help the institutions and teachers in developing a teaching framework which would assist the Arab learners in overcoming their cultural challenges and coping with the same in the western countries like the USA
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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 | 0.001 | 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 | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".