Correlate of Resource Utilization and Students’ Learning Outcome in Colleges of Education in South West Nigeria
Bibliographic record
Abstract
This study investigated resource utilisation in vocational and technical education as a correlate of student learning outcome in selected colleges of education in south west Nigeria. The study examined the relationship between time, space and physical resource utilisation and perceived student learning outcome. The study used the descriptive research design of the survey type. The sample for the study was 687 students. Stratified and simple random sampling techniques were used to select the sample. Student questionnaire on correlate of resource utilisation and perceive student learning outcome was the instrument used to collect data for the study. The data collected were analysed using Pearson Product Moment Correlation and Multiple Regressions. All the hypotheses were tested at 0.05 level of significance. The study reveals that most of the physical resources were well utilised. Time for various activities in vocational and technical education were well utilised except in extra curricular activities and students forum. The perceived students learning outcome was high. There was a significant relationship between resource utilisation and perceived students learning outcome. Time, space and physical resource utilisation were significantly related to perceived students learning outcome. Recommendations were made based on the findings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".