Effect of Availability and Utilization of Physics Laboratory Equipment on Students’ Academic Achievement in Senior Secondary School Physics
Bibliographic record
Abstract
The study determined the available Physics Laboratory Equipment (PLE) for the teaching and learning of physics in senior secondary schools in Nigeria as well as the extent of utilizing the available equipment. The research design adopted for the study was descriptive survey. The sample consisted of nine hundred students who were randomly chosen and fifty Physics teachers who were purposively selected from forty five senior secondary schools in the south western region of Nigeria.Three instruments were used for the collection of data for the study. They are a self-designed questionnaire tagged "Physics Laboratory Equipment Questionnaire" (PLEQ) with reliability of 0.72, a checklist of Physics equipment and Physics Achievement Test (PAT) to measure students’ achievement. The results showed that the optimal utilization of physics laboratory equipment is effective in the teaching of Physics. The federal schools had the maximum adequately utilized PLE and had the highest mean score,followed by the private schools while the public schools with the minimum available equipment and least utilization capacity had the minimum mean score. The study concluded that science laboratory with adequate equipment is a critical variable in determining the quality of output from senior secondary school Physics.
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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".