Academic Procrastination: Psychological Antecedents Revisited
Bibliographic record
Abstract
ObjectiveTaking Beswick, Rothblum, and Mann's seminal paper on academic procrastination as a starting point, we provide an updated review of academic procrastination and consolidate this knowledge with a procrastination typology. The goal of our study was to show that while the degree of procrastination is largely contingent on the trait of conscientiousness, the other four major personality traits determine how procrastination manifests. According to implications of need theory, we operationalised these four traits by the reasons students gave and the activities students pursued while procrastinating.MethodParticipants were 167 students of an undergraduate introductory psychology course. It was designed as a self‐directed computerised course enabled considerable amounts of procrastination. Students filled out a Big Five Inventory and wrote a short essay detailing: (a) what reason they saw as causing them to procrastinate, and (b) what activities they pursued while procrastinating. The reasons and activities were coded according to their fit to the personality traits.ResultsConscientiousness and its facets were the strongest correlates with procrastination. Moreover, in regression analyses, the other personality traits did not incrementally predict procrastination. However, the reasons ascribed to procrastination and the off‐task activities pursued reflected the other personality traits.ConclusionWhile conscientiousness is the core for all procrastination types, the other personality traits determine its phenomenology. Thus, the prominent understanding of a neurotic procrastinator might be misleading for research and practice. In fact, counsellors need to first address the conscientiousness core of procrastination and then match the subsequent interventions to the specific procrastination type.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".