Push Out or Drop Out? Taking a Critical Look at the Poor Performance and Drop-Out of Students of the JSS/JHS Programme in Ghana
Bibliographic record
Abstract
In 1974, new educational reforms aimed at laying a solid educational foundation for children were launched in Ghana. Amongst other things, the reforms recommended the establishment of a three-year Junior Secondary School (JSS) system later renamed the Junior High School system which sought to provide more practical educational skills to students. It aimed to be proactive to the needs of Ghanaians by introducing pre-technical and pre-vocational skills to empower pupils with skills to work with after completion of the JHS program if they are not academically inclined to go further through the Senior Secondary/Senior High School system. After several years of implementation, the success rates of students from the JSS/JHS system have been abysmally poor. The situation is even more abysmal in the rural areas of Ghana where most schools lack basic teaching and learning tools. This paper, from a study of some JSS/JHS schools in some rural and urban settings in Ghana, takes a critical look at some of the factors in this educational system that has pushed many pupils to drop out of the school system hence making the system ineffective and incapable of meeting its intended purpose. DOI: 10.5901/ajis.2014.v3n1p409
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".