Developing Table Tennis Strokes Skill through Learning Method, Feedback and Agility
Bibliographic record
Abstract
This paper presents a discussion on the learning method, feedback and agility needed in the process of developing table tennis strokes skills. The learning method used is the random learning method, which is categorized into regular and irregular. The regular method is developing the table tennis service, drive, smash and lob sequentially and the irregular method is freely choosing the strokes. The feedback, divided into direct feedback through demonstration and direct feedback using the words right or wrong. The agility is categorized into poor, average and good agility. This article gives an overview of the table tennis strokes skill development through the random learning method, using direct feedback and agility. Data were analyzed using ANOVA Scheffe’s test, with the level of significant is set at ? <0.05, and is concluded as follow: 1) the regular random learning method (RLM-r) is significantly different than the irregular random learning method (RLM-ir) 2) the direct feedback using demonstration (DF-d) is significantly different than the direct feedback using right or wrong (DF-rf) 3) the good agility (H-a) is significantly different than average agility (M-a) and poor agility (L-a) 4) there is an interaction between random learning method (RLM), and direct feedback (DF) with agility 5) there is an interaction between random learning method (RLM) with agility 6) there is an interaction between direct feedback (DF) with agility 7) there is an interaction between random learning method (RLM), direct feedback (DF) with agility 8) the combination between regular random learning method (RLM-r) with direct feedback through demonstration (DF-d) with good agility (H-a) is significantly different and is better than the combination of regular random learning method (RLM-r) with direct feedback using right or wrong (DF-rf), with high agility (H-a) 9) the combination between regular random learning method (RLM-r) with direct feedback using demonstration (DF-d) with poor agility (L-a) is not significantly different than the combination of regular random learning method (RLM-r) with direct feedback using right or wrong(DF-rf), with poor agility (L-a) 10) the combination between irregular random learning method (RLM-ir) with direct feedback using demonstration (DF-d) with good agility (H-a) is not significantly different than the combination of irregular random learning method (RLM-r), direct feedback using right or wrong (DF-rf), with good agility (H-a) 11) the combination between irregular random learning method (RLM-ir), direct feedback using demonstration (DF-d), with poor agility (L-a) is not significantly different than the combination between irregular random learning method (RLM-r), direct feedback using right or wrong (DF-rf), with poor agility (L-a).
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".