Academic Self-efficacy and its Relationship to Academic Competitiveness, Academic Procrastination, and Cognitive Flexibility among Undergraduate Students
Bibliographic record
Abstract
This paper aims to explore the relationship between academic self-efficacy, academic competitiveness, academic procrastination, and cognitive flexibility. In addition, it reveals differences in gender, academic specialization, and study level among undergraduate students. The participants were (450) undergraduate students(300) fourth year, and (150) first year at College of Education at a University in Egypt. Academic self-efficacy, academic competitiveness, academic procrastination, and cognitive flexibility scales were used. Descriptive statistics were performed using SPSS. The findings indicated a positive relationship between academic self-efficacy, academic competitiveness, and cognitive flexibility, whereas negative with academic procrastination. In addition, there are significant differences in academic self-efficacy in favor of females, and in favor of males in academic procrastination, but no gender differences in academic competitiveness and cognitive flexibility. The findings also explored significant differences in academic self-efficacy in favor of scientific specialization, and no specialization differences in academic competitiveness, academic procrastination, and cognitive flexibility. The results also revealed significant differences in study level in academic self-efficacy, academic procrastination, and cognitive flexibility in favor of fourth-year students, whereas no differences were found in academic competitiveness. Some recommendations were presented considering the research results.
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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 | 0.003 |
| 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 | 0.001 |
| 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".