Students’ Participation in Quality Assurance Management and Tertiary Institutions Effectiveness in Nigeria
Bibliographic record
Abstract
The study investigated the level of students’ participation in quality assurance management and tertiary institutions effectiveness in Nigeria. Four null hypotheses were formulated to guide the study. The design adopted was correlational research design. The population was made up of 423 2018/2019 session students’ union officials. A sample of 160 was randomly selected from three (University of Calabar, Cross River University of Technology and Federal College of Education, Obudu) public tertiary institutions in the area. The instrument used for data collection was questionnaire (Students’ Participation in Quality Assurance Management Questionnaire (SPQAMQ) validated by experts in test and measurement. It contained 28 items constructed in a 4-point Likert scale. Pearson Product Moment Correlation Statistics was used for data analysis. The result of the analysis revealed a significant positive relationship between students’ participation in decision-making, discipline management, quality assurance committees and school plant maintenance and tertiary institution management when tested at 0.05 level of significance. Based on the findings it was recommended that students should be adequately motivated through quality participation in school governance to enhance their academic achievement and development of basic leadership skills.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".