Problem Solving Skills of Students at the Faculty of Sports Sciences
Bibliographic record
Abstract
The aim of this study is to determine the problem-solving skills of students studying at the Faculty of Sports Sciences of Uşak University and to examine individuals in terms of their personal variables. 290 students, 85 female and 205 male, participated in the study voluntarily at Uşak University Faculty of Sport Sciences. As a data collection tool in the research; “Personal Information Form” and “Problem Solving Inventory (PSI)” developed by Heppner and Peterson were used to determine problem solving skills.According to the normality test results performed to determine the appropriate analysis method for the data, the p-value for the problem solving scale was greater than 0.05. The total scores of the problem-solving scale match the normal distribution. For this reason, while investigating the significant differences, the parametric tests; t-test and ANOVA were used.No significant difference was found between the gender, age variable, monthly income level, monthly income level of families, education level of the parents, the region where the students live, the high school variable that the students graduated from, and the total scores of these students’ problem solving skills (p>0.05). As a result, according to the findings; it has been determined that sports have positive effects on the problem solving skills of the students at the Sport Sciences Faculty.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".