Examination on the Effect of Learning Strategies on Physical Education and Sports Teacher Candidates and Their Motivation of Curiosity and Levels of Exploration
Bibliographic record
Abstract
The puropose of this study is to examine the effect of learning strategies on physical education candidates and their motivation of curiosity and levels of exploration. In accordance with this purpose, the study group is formed by the randomly chosen senior year students of Erciyes, Ömer Halisdemir, Aksaray, Dumlupınar, Gaziantep, Fırat, Selçuk, Ahi Evran, Hacı Bektaşi Veli universities’ Physical Education and Sports Teacher department. Motivated Strategies for Learning Questionnaire, MSLQ (Büyüköztürk, Akgün, Özkahveci and Demirel, 2004), Curiosity and Exploration Inventory-II, CEI (Kashdan et al. 2009) and the ‘Personal Information Form’ which was prepared by the researcher are used in order to collect data in the research. In order to put forth the relation between the scores that were gathered from the scales, Pearson Product-Moment Correlation Coefficient analysis (r) and to determine whether the scores that were gathered are predictive or not Multiple Regression Analysis was applied (β). From the curiosity and exploration sub dimensions, a positive significant relation is found between flexibility and motivation. Furthermore, a positive significant relation is determined between accepting the uncertainty sub-dimension and motivation. A great deal of positive relation is found between the total rates of curiosity and exploration and level of motivation. While there is a positive significant relation between the curiosity and exploration and learning strategies with flexibility, a positive significant relation between accepting the uncertainty sub-dimension and learning strategies is determined too. A positive significant relation is also found between total rates of curiosity and exploration and the use of learning strategies.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.005 | 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".