TEACHERS BACKGROUND AS DETERMINANTS OF PRE-SCHOOL CHILDREN'S WRITING SKILLS IN SOUTHWESTERN NIGERIA
Bibliographic record
Abstract
Abstract The study examined teacher background training as determinants of pre-school children’s writing skills in Southwestern Nigeria. The study adopted mixed design that consisted of observation and correlational design. A self-designed instrument was used for this study. The population for the study consisted of all the pre-school children and their teachers in Southwestern Nigeria. The sample size for the study consisted of 120 pre-school teachers and 600 pre-school children. The study adopted a multi-stage sampling procedure. Three states were selected from six states in Southwestern Nigeria using simple random sampling technique through a ballot system. The instrument used was “Preschool Children Writing Skill Rubric” (PCWSR). Data for PCWSR was a administered by checking children’s’ note books. The data were analysed using Independent Sample T-test. Results indicated that teacher background training significantly influenced preschool children’s writing skills (df = 598; t = 11.476; p < 0.05). The study concluded that teachers’ training background was found to be the factor that could influence the writing skills of preschool children.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".