Exploring of Prospective Teachers’ Metaphoric Perceptions About the Concepts of “Physical Education Course” and “Physical Education Teacher”
Bibliographic record
Abstract
The purpose of this study is to explore the perceptions of prospective physical education teachers on the concepts of “physical education teacher” and “physical education course” through the metaphors. In this study which was structured with the approach of qualitative research, the model of phenomenology, which defines the common meaning of the experience of individuals related to a concept was used. 167 prospective physical education teachers who were enrolled at the department of Physical Education and Sports Education at the Sports Sciences Faculty of Ankara University were included in the study. The content analysis method was used to analyze the data of this study which were collected via the method of semi-structured forms. Within this context, the metaphors formed by the prospective teachers were divided into categories based on their common properties in MS Excel, and a frequency calculation was made. As a result of the analysis, the metaphors that were formed in relation to both the concepts were collected under four categories. It was concluded that the prospective teachers perceived the physical education course as a course that provides children with fun and pleasing moments, relaxes them and is needed for the healthy continuation of their lives, while they perceived a physical education teacher as a person who is found familiar by students, helps them, guides them and is a multi-dimensional person who does everything.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".