Mathematics Students' Coping Behaviour, Happiness, and Self-efficacy in the New Normal: Correlation and K-means Cluster Analysis
Bibliographic record
Abstract
Students in distance education are expected to have low levels of happiness in learning. As such, they must possess coping behaviour and self-efficacy to become motivated in school. This article aims to depict the level of students’ coping behaviour, happiness, and self-efficacy in learning mathematics amid the COVID-19 pandemic and determine their association. Primary data were gathered through Google Forms from 233 available samples of mathematics students at Visayas State University, Baybay City, Leyte, Philippines. The data were summarized through selected descriptive statistics and depicted their relationship with the aid of Spearman rho correlation. In addition, K-means clustering was employed to categorize the students into similar characteristics in regard to coping, happiness, and efficacy. The results showed that students during the pandemic are coping, moderately happy, and possess moderate self-efficacy. The correlation analysis revealed that students’ coping behaviour, happiness level, and self-efficacy are highly and directly associated with each other. This suggests that the students’ coping, happiness, and efficacy levels must go together to achieve a good academic performance in mathematics during distance education. Moreover, the K-means clustering analysis revealed that there are a group of students with significantly lower coping behaviour, happiness level, and self-efficacy in learning. In conclusion, mathematics teachers must encourage their students to engage in the classroom to boost their coping, happiness, and efficacy. Furthermore, teachers must give interesting and realistic mathematics activities, however, doable and suitable for online learning amid the health crisis.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".