Perceptions of, and attitudes towards, English teaching and learning in Cameroon’s technical education
Bibliographic record
Abstract
This study examined the current practices, difficulties and impacts of English as second official language (ESOL) teaching and learning in secondary schools in Cameroon. It investigated the perceptions and attitudes of students, teachers and parents towards the teaching and learning of ESOL, including prevailing teaching and learning practices. This study stemmed from the observation that the exit profile of most students in technical secondary schools does not correspond to the official exit profile set out by the Ministry of Secondary Education (MINESEC). It was therefore necessary to survey students, parents, and teachers with the goal of identifying areas of concern and proposing remedial solutions. Responses of these key stakeholders selected in four education institutions (including two technical high schools and two general high schools) to questionnaires have provided data for the study. Such responses offered insights into the current situation in Cameroon’s ESOL, as well as into the possible utility of, and desire for, the development of ESOL courses aimed at students learning in technical schools. The inclusion and development of English for specific purposes (ESP) in Cameroon’s ESOL teaching and learning could help bring education stakeholders and policymakers closer to what they want to see from the country’s ESOL program.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".