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Record W2910029239 · doi:10.5430/ijhe.v8n1p19

The Effect of Various Local Dances on Prospective Physical Education Teachers’ Attitudes towards the Folk Dance Course

2019· article· en· W2910029239 on OpenAlexvenueno aff
Meryem Altun, Murat Atasoy

Bibliographic record

VenueInternational Journal of Higher Education · 2019
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsDanceFolk dancePhysical educationTest (biology)PsychologyDance educationClass (philosophy)PedagogyVisual artsArtComputer science

Abstract

fetched live from OpenAlex

In this study, the effect of various local dances on prospective physical education teachers' attitudes towards the folk dance course was examined. The research was conducted by using an experimental design with pre-test/post-test control group. A total of 46 female students, which includes an experimental group consisting of 23 students attended the folk dance class and a control group consisting of 23 students did not attend the folk dance class, who are studying at Niğde Ömer Halisdemir University, School of Physical Education and Sports in the Department of Teaching in the spring term of the 2017-2018 academic year and who are aged 20 years, voluntarily participated in the study. In this research, the Folk Dance Course Attitude Scale for Prospective Physical Education Teachers developed by Turan (2015) was used. In the experimental group, the folk dances training program, which continues for 14 weeks with the program of 2 days of a week for 2 hours at each day, was conducted. The data were analyzed by using SPSS 22 (Statistical Package for the Social Sciences) package program. In the analysis of data, dependent sampling t-test was used for examining the differences between experimental and control groups. As a result of the research, it was observed that there was a significant difference in favor of students who took the folk dance training. This result shows that various local dances are effective in developing a positive attitude towards this course.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.488
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.479
Teacher spread0.453 · 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.

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

Citations3
Published2019
Admission routes1
Has abstractyes

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