Relationship Between Learned Resourcefulness and Academic Procrastination in Students Studying in Sports Departments
Bibliographic record
Abstract
The aim of this study was to investigate the relationship between the learned resourcefulness levels and academic procrastination of students studying in sports departments. A total of 372 students who studied in Bingöl University at School of Physical Education and Sports participated in the study as volunteers. In the study, the personal information form, learned resourcefulness scale, and academic procrastination scale were used as data collection tools. In the analysis of the obtained data, Pearson Correlation and Linear Regression analysis were applied by using the SPSS package program. According to the research findings, it was determined that there was a positive relationship between the learned resourcefulness level and the level of academic procrastination, and the learned resourcefulness predicted the academic procrastination level by 8%. As a result; it was concluded that there was a low level and a positive correlation between learned resourcefulness level and academic procrastination, and learned resourcefulness power affects academic procrastination. In this context, it was thought that coping with the difficulties faced by students was important in both achieving their academic goals in school life and maintaining their psychological health.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".