Predictors of Academic Self Efficacy: Intolerance of Uncertainty, Positive Beliefs about Worry and Academic Locus of Control
Bibliographic record
Abstract
Investigation of academic self-efficacy along with intolerance of uncertainty, positive beliefs about worry and academic locus of control is believed to make contributions to the understanding of its complex structure. This study is believed to be of great importance in terms of determining the building blocks to be considered by further research aiming to explain the academic self-efficacy of university students and strengthen their academic self-efficacy. The current study aimed to determine the extent to which the above-mentioned variables predict the academic self-efficacy of university students. The relational survey model was used to reveal the extent to which the above-mentioned variables predict academic self-efficacy. The study was conducted on a total of 717 university students (499 females and 218 males) attending Burdur Mehmet Akif Ersoy University. The data of the current study were collected by using a personal information form developed by the researcher, the academic self-efficacy scale, the intolerance of uncertainty scale, the positive beliefs about worry scale and the academic locus of control scale. In the analysis of the data, Pearson product-moment correlation coefficient and hierarchical multiple regression analysis were used and for this purpose, SPSS 15.0 program was utilized. At the end of the study, it was found that the university students’ academic self-efficacy is positively predicted by positive beliefs about worry and academic internal locus of control and negatively predicted by intolerance of uncertainty and academic external locus of control. The findings of the study were discussed in the light of the related studies previously done by the other researchers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 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".