University students' academic grit and academic achievements predicted by subjective well‐being, coping resources, and self‐cultivation characteristics
Bibliographic record
Abstract
Abstract The connection between grit and achievement in a variety of areas is well documented. Nevertheless, the factors that affect domain‐specific academic grit and the relationship of these factors to academic achievement still require clarification. The present study aimed to explore the contribution of three main categories of variables: subjective well‐being (SWB), coping resources (e.g., self‐efficacy and help‐seeking orientation (HSO)), and self‐cultivation characteristics (e.g., savouring art and personal growth) to academic grit and academic achievement as well as the significance of academic grit as a predictor and mediator for academic achievement. The sample comprised a total of 351 university students between the ages of 18 and 58 from Anglophonic countries (US, Canada, and UK), and Israel. Using structural equation modelling (SEM), academic grit was found to be directly associated with academic achievement among university students while SWB, coping resources, and self‐cultivation characteristics were only indirectly associated with academic achievement with the mediation of academic grit. These results have important educational implications since they reveal existing effects which should serve as a basis for the implementation of university programmes. The results indicate the importance of student well‐being, coping resources, and self‐cultivating characteristics, especially regarding personal growth to perform optimally at university studies.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".