Assessment of Principals’ Supervisory Roles for Quality Assurance in Secondary Schools in Ondo State, Nigeria
Bibliographic record
Abstract
This study identified the nature of principals’ supervisory roles and the perceived effectiveness of principals in the supervision of teachers’ instructional tasks. Furthermore, it investigated the constraints faced by principals in the performance of supervisory duties in the teaching-learning process. This was with a view to providing information on the utilisation of principals’ roles in enhancing quality assurance in secondary schools. The study employed the descriptive survey design. The target population comprised principals and teachers in secondary schools in Ondo state. The sample consisted of 60 principals and 540 teachers randomly selected from 60 secondary schools. The secondary schools were selected using stratified random sampling method from 5 Local Government Area [LGAs]. Three research instruments were used for data collection; they are Principals’ Supervision Rating Scale (PSRS), Interview Guide for Principals (IGP) and Teachers’ Focus Group Discussion Guide (FGDG). Three research questions were resolved based on percentage and mean scores. The results showed that most principals accorded desired attention to monitoring of teachers’ attendance, preparation of lesson notes and adequacy of diaries of work while tasks such as the provision of instructional materials, reference books, feedback and review of activities with stakeholders were least performed by many principals in secondary schools. The study concluded that challenges that principals faced in the tasks of institutional governance, resource inputs, curriculum delivery and students’ learning require effective collaboration and goal-oriented synergetic interrelationship between the school and the relevant stakeholders in its environment.
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 | 0.005 | 0.008 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".