Test Anxiety and Its Relationship to Self-Esteem During the COVID-19 Pandemic
Bibliographic record
Abstract
This research aimed to investigate the level of test anxiety and the relationship between test anxiety and self-esteem among the students of the faculty of Science and Arts in Rafha campus, Northern Border University, KSA during the COVID-19 pandemic. In addition, it intended to identify the differences in test anxiety among the students according to gender variable. The researcher applied the descriptive method, prepared a preliminary data form, and used the Westside Test Anxiety Scale by Driscoll (2007) and the Self-esteem Scale by El Sayed (2019). The data were collected online, and the sample size was (336) students. The results showed that the level of test anxiety among the students of the faculty of Science and Arts during the COVID-19 pandemic was high. The correlation coefficient between the impairment subscale and self-esteem was negative but statistically insignificant. Moreover, the correlation coefficients between both the worry subscale and the test anxiety total scores with the self-esteem resulted in a negative sign and statistically significant. There were significant differences in test anxiety among the students in the impairment subscale, the P-value was (0.005), which indicates that the difference between the two means is statistically significant, and the males’ mean is greater. While the P-value for the worry subscale was (0.226), which indicates that the difference was insignificant, the P-value of the test anxiety total scores was (0.029), which reveals that the difference between the two means is statistically significant, and the males’ mean is greater.
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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 | 0.005 |
| 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 | 0.001 |
| 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".