Predicting Test Anxiety based on the Academic Self-Efficacy, Difficulty in Emotion Regulation and Alexithymia
Bibliographic record
Abstract
Background and Objective: Test anxiety is one of the problems and challenges that students, especially female students, face. Therefore, the present research was conducted with the aim of predicting test anxiety based on the academic self-efficacy, difficulty in emotion regulation and alexithymia. Methods and Materials: The present study was a cross-sectional from type of correlation. The population of this study was female students in the second period of high school in District 1 of Mashhad city in the 2024-5 academic years, which from them 400 people using multistage cluster sampling method were selected as samples. The instruments of the present research were included demographic information form, test anxiety questionnaire (Sarason, 1957), academic self-efficacy questionnaire (Jinks and Morgan, 1999), difficulty in emotion regulating scale (Gratz and Roemer, 2004) and Toronto alexithymia scale (Bagby et al., 1994). The data of this study were analyzed using Pearson correlation coefficients and multiple regression with a enter model in SPSS-27 software at a significant level of 0.05. Findings: The findings indicated that academic self-efficacy had a significant negative relationship with test anxiety and difficulty in emotion regulation and alexithymia had a significant positive relationship with test anxiety (P<0.01). Also, the variables of academic self-efficacy, difficulty in emotion regulation and alexithymia were able to significantly predict 55% of the changes of test anxiety that the contribution of academic self-efficacy was greater than other variables (P<0.001). Conclusion: According to the results of the present research, to reduce test anxiety can provide the basis for increasing academic self-efficacy and reducing difficulty in emotion regulation and alexithymia through educational workshops.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".