Investigation of Physical Education and Sports Students’ Attitudes Towards E-Learning
Bibliographic record
Abstract
The present study aimed to investigate the attitudes of university students who have received sports education towards E-learning. Quantitative research model was applied in the research. The population of the study consisted of 315 students who were selected via random sampling method, at Bayburt University School of Physical Education and Sports. The e-learning attitude scale which was developed by Wilkinson, Roberts and While (2010) and adapted to Turkish by Haznedar and Baran (2012) was used in the study. The data were analyzed through SPSS 22 package program. For descriptive data analysis; ANOVA and Independent Sample T test were applied. The result of the one-way analysis of variance showed that the Physical Education Teaching and Sports Management departments had higher scores than the Coaching Department. In this context, according to the results obtained from the research, it can be said that Coaching Department had lower score than the other two departments because of the fact that there were more courses requiring technical skills compared to the Physical Education Teaching and Sports Management departments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".