Bibliographic record
Abstract
The present research examined the types of self-management strategies associated with academic achievement. Undergraduate students (N=162) completed an online survey that assessed their use of several self-management strategies for studying—including implementation intentions (specific plans for when, where and what to study), self-rewards, and having access to a dedicated, pleasant and well-organized study environment—and their association with the completion of study plans, marks, and ability to concentrate while studying. Regression analyses revealed that study environment was significantly predictive of study plan completion (p<.001), marks (p=.036), and concentration ability (marginally, p=.066), while use of implementation intentions was significantly predictive of study plan completion (p<.001) and concentration ability (p=.005). Use of self-rewards was predictive of study plan completion only (p=.023). Students also rated the extent to which they scheduled both their study activities and leisure activities. Regression analyses indicated that both types of scheduling were significantly predictive of study plan completion (study scheduling: p<.001; leisure scheduling: p=.002) and concentration ability (p=.001 and p=.036, respectively), but were not predictive of marks. These findings have implications for the types of study advice offered to students, especially concerning the possible impact of one’s study environment and the scheduling of both leisure and study activities. Limitations of these findings include the use of self-report measures only and the correlational nature of the results which prevents the determination of cause-and-effect relationships. 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 teacher head, 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".