Relationship between Anxiety and Mental Health of Students Studying Statistics: A Descriptive-Correlational Approach
Bibliographic record
Abstract
Statistics at the college level is one of the most technical subjects that needs to have good mental health so that the students can perform well. This study focused on the investigation of the level of anxiety and mental health of college students in learning statistics. A total of 120 engineering students participated in the survey selected as complete enumeration. Data collection was done through a modified students' statistics anxiety questionnaire and level of mental health. Descriptive measures were computed to describe the data, and regression and correlation analysis to explain its relationship. Results depicted that engineering students have moderate anxiety and they have moderate mental health in learning statistics. This suggests that these students are somewhat anxious but still have a positive learning experience. The correlation and regression analysis revealed that the level of anxiety and mental health of students are negatively but weakly associated, however not statistically significant. This implies that students' anxiety level has somehow adversely affected the mental health of students but its likelihood is negligible. The study strongly suggests that statistics teachers must manage the class well and apply teaching strategies that boost student confidence as well as improve academic achievement. Moreover, teachers should be trained to recognize signs of anxiety and mental health issues and equipped with strategies to support student learning and well-being needs.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".