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Record W3197965435 · doi:10.5430/wje.v11n4p31

The Development of a Pre-Service Teachers Model in Educational Institutions for Students in the Field of Physical Education, Faculty of Education, a Four-Year Program

2021· article· en· W3197965435 on OpenAlexvenueno aff
Jirawat Khajornsilp, Thitipong Sukdee, Aungsumalin Kenjaturas

Bibliographic record

VenueWorld Journal of Education · 2021
Typearticle
Languageen
FieldHealth Professions
TopicSports and Physical Education Research
Canadian institutionsnot available
Fundersnot available
KeywordsFocus groupPhysical educationMedical educationPsychologyDelphi methodFaculty developmentProfessional developmentTeacher educationService (business)Mathematics educationPedagogySociologyMedicineComputer science

Abstract

fetched live from OpenAlex

The purpose of this research was to develop a pre-service teachers model in educational institutions of students in the field of physical education, Faculty of Education, a four-year program, using EDFR (Ethnographic Delphi Futures Research) techniques and focus group teaching techniques conducted by 18 experts and 12 group discussion participants with knowledge and abilities and acting as supervisors, mentors, and heads of professional experience training. The research instruments consisted of a semi-structured interview, questionnaires and group discussion guides. The statistics used in the research were median, mode and interquartile range. The results showed that the development of pre-service teachers in a four-year physical education program consisted of four themes: (a) management of the Faculty of Education, significant in the four-year format, 25 management; (b) organizing the courses, significant in the four-year format and 38 course arrangements; (c) development of students’ competency, significant in the four-year format and development of the 22 competencies; and (d) assessment and feedback, significant in the four-year format and 22-character assessment and feedback.

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)0.001
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 score0.404
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.142
GPT teacher head0.574
Teacher spread0.433 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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