Students’ Motivation, Learning Strategies, and Math Performance in the Modular Distance Learning During the COVID-19 Pandemic
Bibliographic record
Abstract
This study assessed the motivation, learning strategies, and academic performance of Grade 11 modular-based students in General Mathematics in identified secondary schools as a basis for a proposed performance enhancement plan. There were 624 Grade 11 students across three public senior high schools in Cebu who were determined using stratified random sampling. The respondents were asked to answer the adopted survey questionnaire on “Strategies for Learning Questionnaire (MSLQ)” by Pintrich et al. (1991) to assess the learners' motivational levels and learning strategies while their second quarter grades were used as a datum reference to evaluate the strength of correlation. Data gathered were statistically treated using frequency, percentage, weighted mean, rank, and Chi-square test of independence. Results revealed that the respondents had motivational levels classified as moderately motivated; meanwhile, under the learning strategies, the respondents agreed on the methods employed towards learning General Mathematics. The overall general mathematics average among 624 students is 88.44 with a standard deviation of 5.76, indicating that they had very satisfactory performance in General Mathematics. Thus, a performance enhancement plan was crafted to improve both motivational levels and learning strategies.
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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".