Structural relationship of academic self-efficacy, mobile learning readiness, and academic performance among graduate students: a mediation study
Bibliographic record
Abstract
Recent studies indicate that academic performance is a complex phenomenon that can be influenced by various factors. Previous studies have also demonstrated a positive and significant relationship between academic performance, academic self-efficacy, and readiness to take advantage of mobile learning tools. The mediating role of mobile learning readiness in the relationship between academic self-efficacy and academic performance has received scant attention from researchers. The current study has investigated the potential relationship between academic self-efficacy, mobile learning readiness, and academic performance. The study sample comprises 326 students from … University. Data were analyzed by means of structural equation modeling employing AMOS software. Results demonstrated a positive and significant relationship between mobile learning readiness and students’ academic performance. Furthermore, the findings revealed that increased academic self-efficacy was not significantly associated with improved academic performance. Eventually, mobile learning readiness positively mediated the relationship between academic self-efficacy and the students’ academic performance. Considering the key findings of the present study, we suggest implications for developing students’ mobile learning readiness in the mobile learning context.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".