Song-Based Approach to Grammar Learning Among Intermediate Learners
Bibliographic record
Abstract
This study evaluated the grammar competency level of intermediate learners at Sohoton Elementary School, Malimono, Surigao del Norte, through the use of a song-based approach to grammar instruction. It specifically assessed learners’ proficiency in eight grammar components—nouns, pronouns, verbs, adjectives, adverbs, prepositions, conjunctions, and interjections—using pre-test and post-test measures. A quasi-experimental design was employed, involving an experimental group taught with researcher-developed song-based lessons and a control group taught using conventional methods. Data were analyzed using mean, standard deviation, and t-tests. Findings revealed that both groups initially exhibited developing competence in basic grammar categories, with the lowest performance in adverbs and conjunctions. The experimental group showed marked improvement after the intervention, achieving proficient competence in interjections and developing competence in conjunctions and adjectives. In contrast, the control group showed only minimal gains and even declined in some areas. The post-test results indicated a statistically significant improvement in the experimental group, confirming the effectiveness of the song-based approach. Hence, the study concluded that integrating a song-based approach into grammar instruction is both engaging and effective in enhancing grammar teaching and learning. A compendium of these lessons was developed and is recommended as a practical resource for wider adoption by educators.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".