The Importance of Perceived University Life Balance, Hours per Week Engaged in Academic Activities, and Academic Resourcefulness
Bibliographic record
Abstract
The University Life Experience (ULE) scale was created to determine how students utilize their time between academic (class and preparatory) and non-academic (work, social, leisure, and health) activities. In addition to the ULE, 239 undergraduate students completed inventories assessing academic resourcefulness, academic self-efficacy, and university adaptation and satisfaction, along with single item questions asking about perceived academic and non-academic balance and commitment to completing one’s degree. Results indicated that total number of hours spent per week in various non-academic activities was unrelated to most of the variables including academic hours, whereas the number of hours spent per week in academic activities was positively associated with the psychosocial variables and a unique predictor of academic resourcefulness and cumulative grades. Moreover, academic resourcefulness was observed to moderate the relationship between perceived balance and academic hours, such that the average number of hours spent engaged in academic activities per week was greater for students scoring high in academic resourcefulness regardless of whether they had low or high perceptions of balance, especially compared to those students who scored low in both academic resourcefulness and perceived balance. The results suggest that teaching students requisite academic resourcefulness skills to deal with academic challenges assists them in increasing focus on their academic studies as opposed to non-academic activities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".