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Record W4386758408 · doi:10.15294/inapes.v3i2.58391

Pengaruh Kemampuan Gerak Motorik Kasar Terhadap Keberhasilan Belajar Lempar Tangkap Bola Siswa Tunagrahita Di SLB Negeri Wiradesa

2022· article· en· W4386758408 on OpenAlexaff
Muhammad Khamidun, Cahyo Yuwono

Bibliographic record

VenueIndonesian Journal for Physical Education and Sport · 2022
Typearticle
Languageen
FieldHealth Professions
TopicSports and Physical Education Research
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsThrowingBall (mathematics)Mathematics educationPsychologyMathematicsEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

This research is to determine the ability of gross motor movements to successfully learn to catch the ball of lightly impaired students Wiradesa State Out-of-state Schools. This research a one-group pretest posttest design. The sampling technique used was a total sampling .The instruments used in this study used a small ball capture mpar test with a wall target with a distance of one meter. The data analysis used by this study includes prerequisite tests, descriptive analysis and hypothesis tests. The results of this study are related to the results of the t test difference between the pretest group and the wall ball capture throw posttest showing a value of 0.000 < 0.05 which means that the training program has a significant influence on the ability to throw catch the ball. Meanwhile, the t test difference between the pretest group and the posttest throwing the ball back and forth wall showed a value of 0.000 < 0.05, meaning that the training program provided had a significant influence on the ability to catch the ball back and forth. Based on the results of the study, it can be concluded that basic motor movement training affects the success of throwing balls of students at Wiradesa State Out-of-state Schools, Pekalongan Regency, Pekalongan Regency. For teachers, it is hoped that the health assessment teacher can develop and provide an interesting learning method for students to be motivated in participating in teaching and learning activities. This research is to determine the ability of gross motor movements to successfully learn to catch the ball of lightly impaired students Wiradesa State Out-of-state Schools. This research a one-group pretest posttest design. The sampling technique used was a total sampling .The instruments used in this study used a small ball capture mpar test with a wall target with a distance of one meter. The data analysis used by this study includes prerequisite tests, descriptive analysis and hypothesis tests. The results of this study are related to the results of the t test difference between the pretest group and the wall ball capture throw posttest showing a value of 0.000 < 0.05 which means that the training program has a significant influence on the ability to throw catch the ball. Meanwhile, the t test difference between the pretest group and the posttest throwing the ball back and forth wall showed a value of 0.000 < 0.05, meaning that the training program provided had a significant influence on the ability to catch the ball back and forth. Based on the results of the study, it can be concluded that basic motor movement training affects the success of throwing balls of students at Wiradesa State Out-of-state Schools, Pekalongan Regency, Pekalongan Regency. For teachers, it is hoped that the health assessment teacher can develop and provide an interesting learning method for students to be motivated in participating in teaching and learning activities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.002

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.

Opus teacher head0.044
GPT teacher head0.437
Teacher spread0.392 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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