Bibliographic record
Abstract
This article focuses on how learning outcomes of sports skills can be evaluated through field observation. The problem arises when the researcher noticed that the ability of physical education teachers in evaluating skills observation is low. There are some factors that influence this ability. The factors are: they cannot differ between cyclic and non-cyclic movement, they are not able to differentiate technical knowledge that is based on quantity or result, and they have less knowledge to apply evaluation through observation. The purpose of this article is to give solution and alternative to physical education teacher in order to conduct a measurement. There are three aspects involved in evaluating a movement. They are: observing, analyzing, and applying. Furthermore, there are three processes involved in learning psychomotor skills, namely, basic learning stage, concentration, and specialization. An individual’s ability to perform a movement task can be differentiated by age difference, purpose and motivation, movement experience, ability, coordination, and training frequency. This article focuses on the ability and coordination of the performer. Some special characteristics of movements such as movement structure, rhythm, connectivity, width, speed, and accuracy of movements will also be discussed.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".