Teaching Oral English in an ESL Setting: Some Challenges Observed by Teachers in Upper-West Ghana
Bibliographic record
Abstract
This study sought to establish from teachers of English in Senior High Schools in the Upper-West Region of Ghana what they considered to be the main challenges confronting their students in the Oral English course. Thirty-four (34) teachers participated in the survey, which used questionnaire and personal observation for data gathering. The study found that there was a high tendency for features of the L1 of students to affect their learning of L2, especially in the areas of phonemes, morphemes, words, sentences and discourse structures. This was not surprising, as English was the L2 of majority of high school students in the region. The study acknowledges the need for language teachers to understand the linguistic systems of the second language of the student and how they function and be able to differentiate between the first and second language of the learner. It further concedes that a teacher’s ability to speak and understand a language is not a guarantee for attaining the requisite technical knowledge for understanding and explaining the system of the language. It was observed that the challenges of pronunciation were not limited to students but affected the teachers too. The study recommends the teaching of pronunciation at basic level in an ESL context like Ghana’s and also the provision of relevant teaching and learning materials for schools as well as periodic training for subject teachers.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".