Appraising English Language Teachers’ Self-Reports of Readiness to Manage Large Reading Comprehension Classes in Selected Secondary Schools in Ibadan, Nigeria
Bibliographic record
Abstract
This survey was carried out to find statistically valid picture of English Language teachers’ attitudes to the teaching of reading comprehension to large ESL classes in Ibadan metropolis of southwestern Nigeria. The pedagogic practices of the teachers in such a challenging but potentially rewarding situation were also investigated. The far-reaching importance of reading comprehension to the overall success or otherwise of students against their perpetual failure in major examinations justify the study. A total of ninety-eight teachers got through a multi-stage sampling procedure responded to a fourteen-item self-constructed and validate questionnaire. The findings of the data analysis reveal that most of the teachers consider any class of thirty-one students and above is large, the teachers are ill-disposed to large classes, and they do not bother to employ any unusual strategy to make teaching reading comprehension both interesting and highly rewarding to either teacher or students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".