The Effects of Differential Learning Method on the Tennis Ground Stroke Accuracy and Mobility
Bibliographic record
Abstract
The purpose of this study is to investigate the effect of different learning methods on learning tennis stroke skills, retention of learned skills and mobility time compared to traditional learning methods. Twenty-four (12 boys, 12 girls) high school students who have just started tennis education in a high school in Istanbul participated in this study voluntarily (Age: 15.00 ± 0.00 years, weight: 63.46 ± 10.64 kg, height: 1.65 ± 0.06 m, and body mass index 23.26 ± 2.91 kg/m2). Subjects were divided into two homogeneous groups of 12, each with equal numbers of boys (6 girls, 6 boys) according to the pre-test results. One of the groups was named control group, and the other group was named differential learning group. The training sessions were held 3 days a week for 10 weeks and each training lasted 90 minutes. The International Tennis Number (ITN) test was applied to determine the tennis ground stroke accuracy and mobility time. A modified version of the ITN mobility test was applied using the Fitlight TrainerTM device. Repeated Measures Anova test was used to examine the difference between pre-test, post-test and retention test of the same group. One Way Anova was used for the interaction between groups, measurement (pre-test, post-test, retention test) means. p < 0.05 was accepted for the significance level in the interpretation of statistical procedures. As a consequence; It can be said that the differential learning method is more effective than traditional training methods in the accuracy of tennis ground hits, but there is no significant difference between the two groups in retention of learning. Moreover, no significant difference was found in mean differences between groups and from pre-test to post-test and retention test within groups.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".