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Record W2397783467 · doi:10.5539/ass.v12n6p223

Life Skills Acquired in Relation to Teaching Methods Used Through Swimming Context

2016· article· en· W2397783467 on OpenAlexvenueno aff
Manal M. Bayyat, Samira M. Orabi, Mohammad Abu Al-Taieb

Bibliographic record

VenueAsian Social Science · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Life skillsPsychologyBrainstormingScale (ratio)TeamworkMathematics educationSample (material)PopulationMedical educationPedagogyComputer scienceMedicineGeography

Abstract

fetched live from OpenAlex

<p>This study aims: (1) to examine life skills acquired by students attending different levels of swimming courses. (2) To investigate the relation between teaching/learning methods used by swimming teachers and the level of life skills acquired. The population of this study were students who attended one of the three levels of the swimming courses in the Faculty of Physical Education /University of Jordan (n= 236). The sample of the study consisted of 142 students. Both “Life skills through swimming context scale” and “Teaching/ Learning methods in swimming context scale” were used to collect the required data. The data was analyzed using descriptive and statistical analysis. Results showed that the sample acquired the life skills through swimming context on a high level scale. Students believed that communication and self-confidence/self-esteem were the most important skills acquired (86%), followed by teamwork (85%), decision-making/responsibility (84%) and problem-solving (80%). There was a significant relation between life skills acquired and the teaching/ learning methodologies used including brainstorming, demonstration and guided practice, small groups, games and situation analysis. Therefore swimming context using the efficient participatory teaching methods proved to have a positive effect on life skills and youth development.<strong></strong></p>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.764
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.401
Teacher spread0.357 · 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 teacher head, not a consensus.

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

Citations7
Published2016
Admission routes1
Has abstractyes

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