Correlates of Effective Instructional Supervision in Bayelsa State Secondary Schools
Bibliographic record
Abstract
The purpose of this study is to examine the correlates of effective instructional supervision in secondary schools inBayelsa State. A critical examination of all the policies and personnel put in place by the government to achieve theaim of supervision of instruction in secondary school in Bayelsa State were elucidated. The study involved empiricaldesign with the stratified population of fifteen (15) secondary schools, comprising three hundred (300) teachers andsixty (60) supervisors (Principals) randomly selected from three geo-political zones (Sub divided into: Riverine,Upland and Midland). The research instrument used for the study was rating scale consisting of five (3) researchquestions. The analysis involved the use of mean and standard deviation, why the hypotheses were analyzed usingZ-test at 0.05 level of significance.The results of the analysis indicated that: demography, status/personality and perceptions are not a major factor thatinfluences supervision of instruction in schools, but quality and number of teachers, incentives and motivation,quality and number of supervisors, and school location are the correlates factors that influence supervision ofinstruction in schools. Conclusively, the researcher recommends that supervision is very important for effectiveinstruction in secondary schools and that government should provide all it takes to motivate teachers as to enhanceregulation of supervision of instruction.
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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 | 0.005 |
| 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) | 0.002 | 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".