The Impact of Integrating Rhyming Poetry into Vocabulary Instruction Among Primary School Pupils in Ido Local Government, Nigeria.
Bibliographic record
Abstract
This study investigates the impact of integrating rhyming poetry into vocabulary instruction among primary school pupils in Ido Local Government, Nigeria. Employing a quasi-experimental design, the research involved an experimental group receiving poetry-based instruction and a control group following traditional teaching methods. Quantitative data were collected through standardized pre-tests and post-tests measuring vocabulary acquisition, retention, and reading comprehension. Qualitative insights were gathered via classroom observations, structured interviews, and focus group discussions to assess pupil engagement and perceptions of the poetry-based approach. Findings revealed that the experimental group exhibited significant improvements in vocabulary development, phonological awareness, and reading comprehension compared to the control group. Additionally, pupils reported increased motivation and enjoyment during poetry sessions. However, challenges such as selecting culturally appropriate poems and time constraints within the curriculum were identified. It was recommended among others that educational authorities should incorporate rhyming poetry into the language curriculum to enhance vocabulary development and phonological awareness. Teachers should receive training on effective methods for selecting and utilizing poetry in the classroom to maximize its benefits for vocabulary instruction. The study concludes that incorporating rhyming poetry into language instruction can effectively enhance vocabulary development and overall language proficiency among primary school pupils.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".