Science Education In South-West Nigeria: Implications For Educational Policy
Bibliographic record
Abstract
The demand for quality science education requires periodic assessment for its improvement especially in areas with previous challenges. This study was conducted to assess the current status of science education in Ekiti State, Southwest Nigeria. A cross-sectional design, combining a mixed method of quantitative and qualitative data collection approach, was used following a records review. Schools were selected using a cluster sampling technique. Science education status was ascertained by three parameters: students’ pass rates in science subjects in the Senior Secondary School Certificate Examination, science teachers’ profile (assessed with a 14-item self-administered questionnaire) and availability and functionality of infrastructure for science education using an observational checklist. In-depth interviews were conducted among relevant stakeholders using an in-depth interview guide. Quantitative and qualitative data were analyzed with descriptive statistics and a thematic approach respectively. Students’ pass rates were below 50% in Mathematics and Biology but above in Physics, Chemistry and Agricultural Science. The science teacher: student ratio was 1:8 and 37.5% of the teachers were members of the Nigerian Science Teachers Association. One school (8.3%) had a computer library that was adequately equipped. Laboratory facilities were mostly multipurpose in nature. A quarter of the schools had no library while 33.3% had grossly inadequate library facilities. Opinions about science education status among the respondents was widely divergent with some feeling that it was good while others felt otherwise. This study revealed poor status of science education as evidenced by a low science teacher-student ratio and lack of basic infrastructure for science education. Interventions to improve these findings are recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".