Modelling the associations between academic engagement, study process and grit on academic achievement of physical education and sport university students
Bibliographic record
Abstract
OBJECTIVE: The present study examined the impact of academic engagement, study processes, and grit on the academic achievement of physical education and sport university students. METHODS: An internet-based survey recruited 459 university students aged 19-25 years (M = 21 ± 1.3) in physical education and sports (PES) to fill out questionnaires on Physical Education-Study Process Questionnaire (PE-SPQ), Physical Education-Grit (PE-Grit), academic engagement (A-USEI), and Grade Point Average (GPA). A path analysis was carried out to understand variable relationships. RESULTS: Data from each variable exhibited symmetrical and normal distribution, as indicated by the skewness and kurtosis values. The model's fit indices showed sufficient Comparative Fit Index (CFI = 0.92), Tucker-Lewis Index (TLI = 0.90), Goodness of Fit Index (GFI = 0.99) and Normed Fit Index (NFI = 0.90) and showed acceptable levels. The results indicated a statistically significant positive impact of engagement (β = 0.299, p < 0.001) and study processes (β = 0.397, p < 0.001) on academic achievement. However, the effect of grit on achievement was non-significant. CONCLUSIONS: Academic engagement as well as study processes are two important factors predicting academic achievement while grit seems to be not a major predictor. Hence, physical education and sport faculty and university administrators should prioritize student engagement as a determinant of academic outcomes by reforming or redesigning physical education and sport curriculum modules that can facilitate engagement.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".