Correlational Study of Culturally-Based Instructional Strategy and Cognitive Competencies on Problem Solving, Speaking and Listening: An Evidence in Oyo State Nigeria
Bibliographic record
Abstract
The study examined the impact of culturally based Instructional strategy (indigenous songs, rhymes, games, stories, language of the immediate environment and instructional materials) on pre-primary school children’s cognitive competence with special attention to problem-solving, speaking and listening skills. Socio-cultural theory provided the framework, while pretest-posttest control group quasi-experimental design was adopted. 74 children (44 males and 30 females) with a mean age of 5.61 from four pre-primary schools (two public and private schools) were purposively selected from two local government areas, and randomised into CIBS and conventional groups respectively. Children’s Cognitive Competence Rating Scale (r = 0.89) and CBIS Instructional Guide were used to collect the study data. Paired sample t-test and Analysis of covariance(ANCOVA) were used to analyse the data. There was a significant main effect of treatment on children’s cognitive competence (F(1,65) = 10.31; partial ?2 = 0.14). CIBS was found to be potent in enhancing pre-primary school children’s cognitive competence, especially in problem-solving, speaking, and listening skills. Teaching and learning activities at the pre-primary school level should employ a culturally-based instructional strategy.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".