Balanced Scheduling to Reduce Procrastination: Can Scheduling Enjoyable Activities Increase Productivity and Satisfaction?
Bibliographic record
Abstract
The present research examined the effectiveness of a novel scheduling application in which the emphasis is paradoxically on scheduling leisure activities before scheduling work activities. According to the developer of this method, when we neglect to prioritize leisure activities, work activities can become tedious to the point that we begin to procrastinate. To assess this possibility, undergraduate students (n = 27) completed a two-week baseline of their study activities, during which they recorded both the duration and satisfaction level of each study session. This was followed by a four-week intervention in which the participants were randomly assigned to one of two conditions: (1) a traditional scheduling condition in which they scheduled their study activities for the coming week, and (2) a balanced scheduling condition in which they scheduled their leisure activities first and study activities second. We found no significant difference between the traditional scheduling and balanced scheduling groups in either study duration or satisfaction. For the combined group, however, we did find an overall effect of scheduling, with improvements from baseline to treatment in study duration (p < .001) and study satisfaction (marginally significant, p = .07), as well as an overall decrease in scores from pre-study to post-study on a standardized measure of academic procrastination (p = .01). These findings suggest that weekly scheduling, regardless of whether it includes scheduling one's leisure activities, may be an effective intervention for improving students' study behaviour and reducing procrastination. Limitations of the study include a small sample size and lack of appropriate controls for possible confounds. Discipline: Psychology (Honours) Faculty Mentor: Dr. Russ Powell
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".