The importance of self‐efficacy and educational aspirations for academic achievement in resource‐limited countries: Evidence from Ghana
Bibliographic record
Abstract
INTRODUCTION: Research on the influence of academic self-efficacy and educational aspirations on academic performance is underdeveloped in resource-limited countries. This study replicates and expands on earlier research that investigated a complex network of relationships between academic self-efficacy, educational aspirations, and academic performance. METHODS: Data from 4282 adolescents in Ghana and path analysis were used to test the causal pathways, and path invariance analysis was used to assess the moderation role of gender. Instrumental variable techniques were used to validate the path models. RESULTS: Increase in academic self-efficacy indirectly accounts for improvement in academic performance through the mediational role of educational aspirations. The effects of self-efficacy on educational aspirations, and educational aspirations were stronger for boys than for girls. CONCLUSIONS: These findings suggest that in resource-limited countries where the financial burden of schooling tends to be a demotivating factor, interventions that target adolescents' academic self-efficacy may be an effective means to boost educational aspirations and academic performance. Interventions should be tailored to meet the needs of all students so that all children can think of school as an important part of their lives and aspire to achieve, now and in the future.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".