MétaCan
Menu
Back to cohort
Record W3081787727 · doi:10.5430/wje.v10n4p149

Critical Thinking Skills and Self-Efficiency Beliefs in Preservice Physical Education Teachers

2020· article· en· W3081787727 on OpenAlexvenueno aff
İsa Doğan, Erdal Zorba, Musa Şahin

Bibliographic record

VenueWorld Journal of Education · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicEducation Practices and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMathematics educationPhysical educationCritical thinkingScale (ratio)Structural equation modelingScope (computer science)Sample (material)Statistical analysisSelf-efficacyTeacher educationPedagogySocial psychologyGeography

Abstract

fetched live from OpenAlex

The aim of this research is to determine the relationship between preservice physical education teachers' self-efficacy beliefs and critical thinking tendencies. For this purpose, our universe constitutes preservice physical education teachers studying at different universities in the 2018-2019 academic year. The research sample consists of 640 preservice teachers in total, 350 males and 290 females. Cities in which the preservice teachers are involved in the research and the universities where they are studying; It consists of 8 provinces: Bartın, Bolu, Çorum, Düzce, Karabük, Kastamonu, Sinop, Zonguldak. Within the scope of the research, “California Critical Thinking Scale (CCTDI)” and “Teacher Self-Efficacy Scale” were used to obtain the data collected from preservice teachers. The data collected for the purpose of the research were analyzed with the SPSS-25 statistical program. Structural equation modeling analyzes were carried out using the data collected from 640 participant groups using the AMOS-25 package program. As a result, students can be directed to earn these trends through activities aimed at gaining critical thinking skills and tendencies by rethinking physical education and sports school programs being implemented in our country. In this regard, university students can be given the opportunity to become highly critical individuals.

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)0.000
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.024
GPT teacher head0.316
Teacher spread0.292 · 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 designQualitative
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

Citations4
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueWorld Journal of EducationSame topicEducation Practices and ChallengesFrench-language works237,207