Estimation of the Predictive Powers of Teachers’ Pedagogical Skills on Pupils’ Academic Achievement in Christian Religious Studies in South-East, Nigeria
Bibliographic record
Abstract
The general purpose of this study was to determine teachers’ pedagogical skills as predictors of pupils’ academic achievement in Christian Religious Studies in South East, Nigeria. The study adopted a correlation research design. Two questions were posed and two hypotheses were formulated and tested for the study. The study was carried out in all the primary schools in South-East, Nigeria. The population of the study included all the 42,796 primary school teachers in South-East, Nigeria. A sample of 855 public primary school teachers was drawn for the study using a multi-stage random sampling procedure. Two instruments were used for data collection, they are: Teachers’ Pedagogical Skills Questionnaire (TPSQ) and Pupils’ Academic Achievement Proforma (PAAP). The instruments were validated by three experts. The reliability of the instrument was established by trial-testing on 20 primary school teachers that were randomly drawn from Ezinihite-Mbaise Local Government Area of Imo State which is a part of the study area but not a sampled area. Cronbach’s Alpha Reliability Coefficient was used to determine the internal consistency reliability index 0.89 for TPSQ. Data collected were analyzed using Correlation Coefficient and Regression Analysis of Variance was used to test the hypotheses at 0.05 level of significance. The findings revealed that teachers’ pedagogical skills significantly predicted pupils’ academic achievement in Christian Religious Studies in South-East. It was recommended that teachers should possess sufficient knowledge and skills regarding curriculum content and classroom management in order to overcome classroom challenges.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".