Teachers’ Demographic Variables as Predictors of Critical Thinking Skills of School Children: Implications for School Counselling
Bibliographic record
Abstract
This study explored the extent to which teachers’ demographic variables predict the critical thinking skills of school children, and the educational implications. The study was guided by research questions and null hypotheses, which used a correlation survey design. The population size was 17,928 middle basic pupils in all government-owned schools in Enugu State, Nigeria. Out of the target population, 1,400 pupils were selected using a multistage sampling technique. The instrument used was theCornell Class-Reasoning Test, Form X, which contains 72 items and assessed the respondents’ critical thinking skills. The data collected was analyzed using Statistical Packages for Social Sciences (SPSS) version 20. Major findings revealed that teachers’ gender does not significantly predict the critical thinking skills of primary-school children in Enugu State, and that teachers’ age does not predict critical thinking of primary school children to a large extent. Last, it was found that teachers’ location does not predict the critical thinking skills of primary schoolchildren to a large extent. Based on the findings, counseling implications and recommendations are made.
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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 | 0.012 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".