The Value of Songs and Rhymes in Teaching English to Young Learners in Saudi Arabia
Bibliographic record
Abstract
The study aimed to show the impact of using songs and rhymes in teaching English to young female learners in Saudi Arabia. It involved 20 Saudi teachers who were randomly selected from public and private schools in Riyadh city. The age of the female students ranged from 6 to 10 years. Forty parents volunteered to participate, Parents were asked to answer an online survey comprising ten different questions. Interview questionnaire and online survey were the tools used for data collection. About 9 of all teachers don’t use songs and rhymes activities in teaching English. 15 of teachers out of 20 said that it is not a mandatory part of the curriculum. 13 of the teachers believe that it is very important and 2 teachers believed in using songs and rhymes to facilitate remembering. 16 of teachers out of 20 noticed that their students are actually using the songs or their vocabularies outside the classroom and 17 of all teachers stated that songs and rhymes helped their young learners’ English language development. 82.50% of parents in Saudi Arabia support teaching English to their children, 47.50% of parents stated that their child is using English only in the classroom. Only 7.50% of the parents were not aware of this classroom activity while 92.50% of them are aware. 2.50% of parents expressed their disagreement. The study found out that songs and rhymes are rarely used in teaching English to young learners in the Saudi Arabia and curriculum was not rich enough with activities like songs and rhymes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".