Effects of Various Mathematics Interventions to the Learners' Motivation and Numeracy Skills of the Secondary School Learners in the Division of Lucena City
Bibliographic record
Abstract
This study explores the effects of various mathematics interventions on the motivation and numeracy skills of secondary learners in Lucena City. The researcher utilized a descriptive research design using the quantitative research approach. The study employs surveys to collect primary data from students and teachers. Additionally, secondary data on learners' numeracy levels and quarterly grades are analyzed to comprehensively assess the intervention's effect. The paired sample statistics showed an increase in mean scores from the first quarter (M = 78.97) to the second quarter (M = 80.03), suggesting measurable progress in numeracy proficiency. A strong positive correlation (r = 0.885, p < .001) between the first and second quarters confirmed the consistency of this improvement. Moreover, the paired-samples t-test indicated a statistically significant difference (t = -4.84, p < .001), demonstrating that the intervention program had a meaningful impact on learners' mathematics performance. Furthermore, the study reveals increased students' motivation and mathematics engagement, suggesting a positive correlation between structured intervention strategies and learner outcomes. The study concludes that well-designed and sustained mathematics interventions are essential in addressing learning gaps, improving numeracy proficiency, and fostering a positive learning environment. It recommends continuously refining intervention programs and incorporating data-driven strategies to ensure their effectiveness in enhancing academic performance and motivation in mathematics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".