The detrimental effect of academic procrastination on subsequent grades: the mediating role of coping over and above past achievement and working memory capacity
Bibliographic record
Abstract
BACKGROUND: Academic procrastination is common among university students and its effect on their achievement is worrisome. Although procrastination is often depicted as self-regulation failure, research still needs to examine the self-regulatory mechanisms involved in the relationship between procrastination and achievement. OBJECTIVES: In this prospective study, we sought to (a) unravel the unique effect of academic procrastination on university grades, (b) examine the mediating role of task-oriented and disengagement-oriented coping as a self-regulatory pathway toward achievement, (c) control for the potential confounding influence of past achievement and working memory capacity. METHODS: A sample of 258 university students completed self-report measures of academic procrastination and coping, and performed tests of working memory. Their semester grade point average was collected at the end of the semester. RESULTS: Results of structural equation modeling showed that academic procrastination negatively predicted subsequent academic achievement, even after controlling for high school achievement and working memory capacity. Furthermore, indirect effects revealed that task- and disengagement-oriented coping explained 70% of the total effect. CONCLUSION: These findings outline that the effect of academic procrastination cannot be reduced to a history of academic difficulties or limited cognitive ability and that coping plays an important role in the procrastination - achievement relationship.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".