Perceived Self-Efficacy and Its Relationship to Achievement Motivation among Parallel Program Students at Prince Sattam University
Bibliographic record
Abstract
The study aimed to identify the level of perceived self-efficacy and achievement motivation and the relationship between them. The sample of the study included 164 students. The researcher used the measure of perceived self-efficacy and the measure of achievement motivation. The measure of perceived self-efficacy consisted of 72 paragraphs distributed on nine dimensions. While the measure of achievement motivation consisted of 20 paragraphs. It has been conducted the necessary honesty and reliability coefficients for them.The results indicated to the high levels of perceived self-efficacy and achievement motivation, to a correlation which is very weak and a positive direction (proportional) with statistical significance between perceived self-efficacy dimension (behavioral) and achievement motivation, to six correlation relationships which are weak and a positive direction (proportional) with statistical significance between perceived self-efficacy dimensions (emotional, social, self-confidence, others-confidence, cognitive, moral) and achievement motivation, to the presence of correlation relationship which is medium and positive direction (proportional) with statistical significance at the significance level (α = 0.05) between perceived self-efficacy and its two dimensions (persistence and perseverance, academic) and achievement motivate.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".