Learning Approach for Hand Eye Coordination and the Kinesthetic Outcomes of Learning to Throw-Catch a Ball
Bibliographic record
Abstract
The purpose of this study was to investigate the effects caused by the methods of teaching and eye-hand coordination to kinesthetic outcomes of throwing and catching a ball. The population of this study was 149 third grade elementary school students in a village in East Jakarta. A sample of 80 students was chosen using random cluster sampling. Data was analyzed using analysis of variance (ANOVA) and the significance level is set at 0.05. The results showed that the kinesthetic of ball’s throw-catch by students who were taught with play teaching method is higher than the students who were taught with the guided teaching methods. There is an interaction between teaching methods, gender and hand eye coordination towards the kinesthetic outcomes of throwing and catching a ball. The kinesthetic result of ball’s throw-catch for male students who were taught with play teaching methods is higher than the male students who were taught with guided hand-eye-coordination teaching method. The kinesthetic result of ball’s throw-catch for female students who were taught with play teaching methods is higher than the female students who were taught with guided hand-eye-coordination teaching method. The kinesthetic result of ball’s throw-catch for male students with low hand-eye coordination taught with guided teaching method is higher than those who were taught with the play teaching method. The kinesthetic result of ball’s throw-catch for female students with low hand-eye coordination taught with the guided teaching method is higher than those taught with the play teaching method.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".