Home-Based Enrichment Activities for Science 9 (Biology): Effects on Students’ Academic Performance and Science Engagement
Bibliographic record
Abstract
This study aimed to develop Home-Based Enrichment Activities specifically designed for Science 9 and assess their impact on academic performance and engagement of Grade 9 students attending public high schools in the Philippines during the first quarter of the school year 2022-2023. This study employed both descriptive and quasi-experimental research methods. The instruments included the validated pre-test and post-test assessments, a survey questionnaire for teachers, and a survey sheet for students. The data was collected from (60) students, (15) high school teachers and (15) master teachers in science in public schools. The data gathered were statistically analyzed using weighted mean, percentage, independent-samples t-Test, paired t-Test, and z-Test. Results showed that the control group exhibited lower mean scores (4.33 and 29.33) in the pretest and posttest compared to the experimental group (13.97 and 38.20), indicating a significant improvement in performance of the students, further affirming the positive impact of the developed Home-based enrichment activities on academic performance. The experimental group also expressed a high level of science learning engagement, demonstrating strong involvement, effort, and preparation in science lessons as manifested by the grand weighted mean of 3.88. Recommendations include incorporate home-based enrichment activities into blended learning modalities, particularly in modular distance learning to help students to learn the lesson and answer their modules on their own and increase students’ achievement in Science. Additionally, these supplementary activities can also provide valuable support to students who are at risk of failing a specific subject due to time constraints and challenging learning tasks within their modules.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".