Developing Academic Achievement in Mathematics on Fractions through Active Learning Combined with Skill Practice for Grade 3 Elementary Students
Bibliographic record
Abstract
This research aims to 1) develop an effective Active Learning plan combined with skill practice according to the 70/70 criterion, 2) study the learning achievement index through Active Learning combined with skill practice, and 3) investigate the satisfaction towards Active Learning combined with skill practice. The sample group consisted of 27 Grade 3 students from Muang Wapi Pathum School, during the first semester of the 2023 academic year, selected through purposive sampling. The research tools included 1) 10 Active Learning plans totaling 10 hours, 2) mathematics skill practice, 3) an achievement test consisting of 15 multiple-choice questions, and 4) a satisfaction questionnaire regarding Active Learning combined with skill practice, using a 5-point Likert scale with 13 items. The statistical analysis included mean, percentage, standard deviation, and effectiveness index. The research found that the Active Learning plan was effective with an efficiency of 81.00/82.20, which meets the predefined criterion of 70/70. The effectiveness index was 0.6828, indicating that students improved their learning by 68.28 percent. Overall satisfaction was at the highest level, with an average score of 4.54.
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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 | 0.001 | 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) | 0.001 | 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".