Motivation and Performance of Students in School Physical Education in Which Mobile Applications Are Used
Bibliographic record
Abstract
The aim of the text is to discuss the use of technologies in Physical Education (PE) at schools. The research focused on the pupils of an upper-primary school/lower-secondary school, who were given experimental PE for a period of 10 weeks. The research objective was to identify typical groups of students on the basis of their physical performances and motivation. Unifittest 6-60, a standardized motor-skills test, was used to measure physical performances, and the Czech translation of SIMS, a Canadian–American standardized test, was used to specify the degree of motivation. Based on the obtained data, the method of cluster analysis identified three typical groups of pupils. These three groups differ in their approach to the use of mobile applications in the process of PE. The research results show that thanks to the implementation of mobile phones in the process of PE and thanks to a different approach taken by the teacher, increased internal motivation and an increase in identified regulation can be seen, as well as a decrease in amotivation and a rapid increase in motor performances, especially in the case of students whose performances are average or below average in usual PE classes.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".