Factors Affecting Stress of Online Learning due to the COVID-19 Situation at Faculty of Education, Thailand National Sports University Chonburi Campus
Bibliographic record
Abstract
The purpose of this research was to study factors affecting stress of online learning due to the COVID-19 situation at the Faculty of Education, Thailand National Sport University Chonburi Campus, and to create equations to predict the stress of students. The samples consisted of 280 students in the Faculty of Education, Thailand National Sport University Chonburi Campus. The research instruments were the Suanprung Stress Test and a questionnaire gauging emotional and mental status, perceived severity of stress, opportunity for risk of stress, perceived usefulness of stress management, university policies that promote stress management, environment, and social support. The data were analyzed in terms of frequency, percentage, mean, standard deviation and Stepwise Multiple Regression Analysis. The results indicated that 1) study stress levels during the COVID-19 situation were at a moderate level: subjects had a mild level of stress 8.93 percent, moderate level of stress 56.78 percent, high level of stress 33.93 percent and severe level of stress 0.36 percent; 2) The subjects’ emotional and mental well-being, perceived severity of stress, perceived usefulness of stress management, University policies that promote stress management, environment, social support were at a high level, and Opportunity for risk of stress moderate level. 3) The factors that related to the stress of online learning due to the COVID-19 situation comprised were 4 variables: achievement perceived usefulness of stress management, opportunity for risk of stress, university policies that promote stress management, and social support. There was a statistically significantly difference at the 0.1 level. These 4 factors could predict the Stress about percentage of 70.50. The significantly predicted equations were as follows: In term of raw scores: Y/ = 75.425 + (-5.180) (X4) + 3.816 (X3) + (-3.465) (X5) + (-2.689) (X7) In term of standard scores: Z / Y = (-0.324) (Z X 4) + 0.280 (Z X 3) + (-0.225) (Z X3) + (-0.165) Z X 7)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".