The Role of Metacognitive Beliefs in Predicting Academic Procrastination Among Students in Iran: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Academic procrastination is a challenge that many students face. Metacognitive beliefs are the main cause of academic procrastination because they are one of the main reasons for students' academic failure or progress. OBJECTIVE: This study aimed to determine whether and to what extent academic procrastination could be predicted based on students' metacognitive beliefs. METHODS: This descriptive cross-sectional study involved 300 students selected via stratified random sampling. Data were collected using the Procrastination Assessment Scale for Students and the Metacognition Questionnaire-30. The data analysis was done using the Pearson correlation coefficient and regression analysis to estimate the correlation coefficient and predictability of academic procrastination based on metacognitive beliefs. RESULTS: A significant negative correlation was observed between the subscale of positive beliefs of concern and academic procrastination (r=-0.16; P<.001). In addition, the metacognitive beliefs of the participants predicted 10% of academic procrastination. The component of positive metacognitive beliefs with the β value of 0.45 negatively and significantly predicted the students' academic procrastination (P<.001), whereas the component of negative metacognitive beliefs with the β value of .39 positively and significantly predicted the students' academic procrastination (P<.001). CONCLUSIONS: Metacognitive beliefs can predict students' academic procrastination. Therefore, the modification of metacognitive beliefs to reduce procrastination is suggested.
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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 | 0.005 |
| 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 | 0.001 | 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".