Students’ Self-Efficacy and Self-Rating Scores as Predictors of Their Academic Achievement
Bibliographic record
Abstract
This study set out to establish the predictive power of students’ self-efficacy and self-rating scores on undergraduate students’ academic achievement in a Psychology course (psychology of Learning). The correlational research design was adopted and all the 133 sophomores in the Continuing Education Programme (CEP) of the Nnamdi Azikiwe University, Awka, Nigeria who registered the course were sampled for the study. The instruments used were a domain-specific self-efficacy questionnaire and a semester examination developed by the researchers. The regression analysis showed that self-efficacy and self-rating did not combine to predict students’ achievement; however, considering their relative contribution, students’ self-rating scores predicted their academic performance more than their self-efficacy. Furthermore, students’ self-efficacy and self-rating scores were related, but only students’ self-rating scores were related to their academic achievement. It was concluded that large self-efficacy is not enough to counter limited knowledge and competence. DOI: 10.5901/jesr.2014.v4n3p223
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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 | 0.008 |
| 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) | 0.001 | 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".