Bibliographic record
Abstract
Research has shown that self-control is a vital component of scholastic achievement. A major problem students’ face, however, is that they often succumb to the temptation of more immediately available rewards, thereby opting to put off study behaviours. Various self-management tactics are often recommended for studying improvement. Little is known, however, about what may be the ‘best’ tactics for students to implement. Thus, the present research examined the relative efficacy of several self-management tactics commonly recommended to improve studying and academic performance. Undergraduate students (N=689) at a small Canadian university completed a survey examining the association between selected self-management tactics and students’ tendency to complete intended study plans, ability to concentrate while studying, tendency to procrastinate, grade point average, and students’ perceived automaticity (“habit strength”) of sitting down to study. Regression analyses indicated that the use of a dedicated study environment, implementation intentions, and contingent self-reward were the most beneficial tactics for improving study behaviour and academic performance. A limitation of this study is the correlational nature of the results. Further experimental research is needed to properly determine the relative efficacy of these tactics for improving study behaviour and academic performance. Faculty Mentor: Russ Powell Department: Psychology (Honours)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.069 | 0.019 |
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".