Modeling Students' Self-Efficacy in Mathematics during the Covid-19 Pandemic
Bibliographic record
Abstract
Self-efficacy in learning mathematics helps the student to overcome difficulties and challenges in problem-solving during unprecedented times. This article aims to measure the level of students' self-efficacy and its determinants during the COVID-19 pandemic in learning mathematics online. The study considered primary data from 233 students selected in a non-random approach at Visayas State University, Baybay City, Leyte, Philippines through the aid of an online survey. The data were analyzed using some descriptive statistics calculation and regression analysis was used to model the students' self-efficacy and its factors. Results showed that, on average, the students' self-efficacy level is considered "moderate" amidst the pandemic. This means that most of these students are still having mathematical anxiety and experiencing hindrances in achieving good academic performance in mathematics online. The statistical model revealed that the demographic and learning profile of students is not significantly influencing the level of self-efficacy. In addition to that, the mathematics teachers' intervention has shown also an insignificant influence on the students' self-efficacy. In conclusion, students during the pandemic are having difficulty adopting a new type of learning (distant/online) due to their moderate level of self-efficacy. Hence, the study recommends that teachers must make the learning environment exciting and interesting to boost the students' motivation and self-efficacy in doing their mathematics tasks. Furthermore, teachers must give mathematics activities that are suitable and doable for online learning that enhances students' creative thinking.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".