Examining the Contributions of Coping Self-Efficacy and Help-Seeking Behaviour on Academic Performance
Bibliographic record
Abstract
Self-regulated learning (SRL) has become an essential aspect of education, with a focus on improving students' skills and strategies to learn and perform effectively. The purpose of this study was to investigate the mediating role of instrumental help-seeking in the relationship between coping self-efficacy and academic performance from a SRL perspective. Participants (N=233) were enrolled in an elective educational psychology course at a Western Canadian University and completed weekly self-assessments related to SRL practices (e.g., coping self-efficacy, time management, help-seeking behaviours). Path analyses using structural equation modeling were used to examine the mediating role of instrumental help-seeking behaviour on the relationship between coping self-efficacy and academic performance. Findings revealed that coping self-efficacy was not significantly related to academic performance, and that instrumental help-seeking behaviour did not mediate this relationship. However, subsequent models showed that while coping self-efficacy may not directly impact students' GPA, it does influence their help-seeking behaviors. Specifically, the results demonstrated that students with low coping self-efficacy tend to avoid seeking help and perceive it as a threat. Notably, only executive help-seeking behavior had a negative association with GPA, suggesting that relying on others to solve the task may have a detrimental effect on academic performance. Overall, this study offered valuable insights into the role of coping self-efficacy and help-seeking behaviors in academic settings, emphasizing the need for further research to investigate the underlying factors contributing to the negative association between executive help-seeking and GPA.
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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 | 0.009 |
| 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) | 0.002 | 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".