The implementation of E-learning games in teaching Edukasyong Pantahanan at Pangkabuhayan (EPP) and the academic outcomes of the Grade 5 learners of Kabakahan Elementary School, Agusan Del Sur division: A pre-experimental approach
Bibliographic record
Abstract
This study examined the effect of implementing e-learning games in teaching Edukasyong Pantahanan at Pangkabuhayan (EPP) on the academic outcomes of Grade 5 learners at Kabakahan Elementary School in Agusan del Sur. The research employed a quantitative design, specifically a pre-experimental one-group approach, with 23 learners participating in the intervention. The level of e-learning game implementation was assessed across six factors: Concentration, Goal Clarity, Feedback, Immersion, Social Interaction, and Knowledge Improvement, while academic outcomes were compared between the first and second quarters using descriptive statistics and percentage difference analysis. Findings indicated that the overall implementation level of the e-learning games was rated as ‘High’ (Mean = 4.16), interpreted as ‘Oftentimes Implemented’, suggesting the games contributed positively to learner engagement and knowledge improvement. Crucially, academic outcomes improved after exposure to the e-learning games, evidenced by the percentage of learners achieving a Very Satisfactory rating (88-94) increasing significantly from 17.39% in the first quarter to 43.48% in the second quarter, a positive difference of +26.09%, with concurrent decreases in lower performance categories. The study concludes that the implementation of e-learning games was effective and demonstrated potential instructional value for enhancing learner motivation and academic performance in EPP, supporting the integration of technology-enhanced learning tools to improve learning outcomes for elementary learners.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".