Challenges in the Teaching and Learning of Agricultural Science in Selected Public Senior High Schools in the Cape Coast Metropolis
Bibliographic record
Abstract
The study was conducted in selected Public Senior High Schools in the Cape Coast Metropolis aimed at investigating the challenges in the teaching and learning of Agricultural Science. In all a sample of 78 respondents involving 60 Agricultural Science students and 18 Agricultural Science teachers were involved. The research instrument used for the data collection was questionnaire which was developed by the researcher in two different forms, one for the Agricultural Science students and the other for the Agricultural science teachers. Research findings from the study indicated that the major challenges facing the teaching and learning of Agricultural Science include frequent use of lecture method in teaching, large class size and poor remuneration of teachers. Others include inadequate teaching and learning materials and their availability, difficulty in planning field trips as well as laziness and truancy on the part of teachers. However, it must be emphasized that motivational factors such as one’s own interest, having a role model, future ambitions and the supply of adequate textbooks positively affect the teaching and learning of Agricultural Science in Public Senior High Schools in the Metropolis. The study recommends that parents must be educated to understand the important role and prospects the learning of Agricultural
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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 | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".