The Utilization of Jigsaw Strategy in Teaching Health among Grade 3 Students
Bibliographic record
Abstract
In this quantitative quasi-experimental research, the researchers attempted to discover the effectiveness of the Jigsaw strategy, a kind of cooperative learning, to the 25 Grade 3 students, with 16 male and 9 female, at College of San Benildo – Rizal in enhancing their learning process especially in familiarizing and understanding concepts of a particular topic (Factors that Influence Consumer’s Choice of Goods and Services) in Health, one of the components of their MAPEH subject, for the fifth week of the fourth quarter this school year 2022-2023. The researchers administered a validated 15-item pretest prior to the implementation of the Jigsaw strategy to measure their background knowledge about the said topic. During the face-to-face implementation of the Jigsaw strategy, the researchers grouped the students into two sets of groups (expert group and home group). There were five home groups and five expert groups, with five members each. Each member of the expert group was assigned with a sub-topic to study with their group mates and share what they have learned to their group mates in the home group. After two days of face-to-face Jigsaw strategy implementation, the researcher administered a validated 15-item posttest to measure the knowledge and understanding of the Grade 3 students about the topic with the use of the Jigsaw strategy. Results revealed that both male and female students’ mean scores in the posttest increased. Based on the paired t-test results, there was a significant difference between the pretest and posttest scores of the Grade 3 students with the P value of < .00001. In conclusion, based on the findings presented, the Jigsaw strategy is effective in improving the Health test scores of the Grade 3 students of this study.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".