Teachers’ Professional Development and Quality Assurance in Nigerian Secondary Schools
Bibliographic record
Abstract
This study examined the relationship between teachers’ instructional tasks and their qualifications and teaching experience. The descriptive survey design was used in the study. Respondents included 60 principals and 540 teachers randomly selected from 60 secondary schools. Selection of the secondary schools was based on stratified random sampling method. Data were collected using Teachers’ Instructional Task Performance Rating Scale (TITPRS), Interview Guide for Principals (IGP) and Teachers’ Focus Group Discussion Guide (TFGDG). Data collcted were analysed using Pearson product moment correlation statistics. There were significant relationships between teachers’ qualifications and instructional task performance (r = 0.681 at p < 0.05), and between teachers’ teaching experience and instructional task performance (r = 0.742 at p <0.05). The study concluded that teachers’ instructional task performance can be enhanced with a good qualification and experience in teaching, while the challenges that teachers face in the tasks of instructional inputs and curriculum delivery require effective capacity development during service, so as to improve the quality of teaching in secondary schools and the overall quality of the education system.
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 | 0.003 | 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".