TEACHER CHARACTERISTICS THAT INFLUENCE DEVELOPMENT OF ORAL LANGUAGE SKILLS AMONG PRE-PRIMARY SCHOOL PUPILS IN NAIROBI CITY COUNTY, KENYA
Bibliographic record
Abstract
This article presents the findings from our investigation of teachers’ characteristics that influence development of oral language skills among pre-primary pupils. The study was conducted in 83 schools in Kibra Sub-County, Kenya. Questionnaires and observation schedules were used to collect data. Data was analysed using SPSS. The main findings of the study indicate that teaching strategies that were mostly used by pre-primary school teachers were code-switching, examples, repetition, substitution and explanation. On the other hand, questions, direction, expansion of children words and contrast were the least used teaching strategies when teaching oral language skills. The study revealed that the there is a slight correlation between the type of training teachers received and the teaching strategies they used as most of the DICECE (District Centres for Early Childhood Education, Kenya) trained teachers used more teaching strategies when teaching oral skills compared to non-DICECE teachers. The findings also revealed that there was some correlation between teacher’s academic qualifications and their use of a few teaching strategies. There was also some correlation between teaching experience and the use of a few teaching strategies. Since the strategies used by pre-primary school teachers under the study were less than half of the recommended teaching strategies to promote oral skills, the study recommends that teachers should be encouraged to use more in structural strategies to improve children’s oral language skills.
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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 | 0.003 |
| 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".