Subject Matter Competency Perceptions of Teacher Educators in Education Faculties of Turkey
Bibliographic record
Abstract
The purpose of the current study is to determine teacher educators’ level of general subject matter competency perception and to investigate whether this level varies depending on different variables. In the collection of the data of the study employing the survey model, a single dimension and 106-item “scale of teacher educators’ general subject matter competency perceptions” was used. The scale was prepared in the online environment and sent to 8200 faculty members working in education faculties all over Turkey by e-mail. A total of 789 teacher educators responded to the scale. It was found that the teacher educators generally consider themselves highly competent in terms of general subject matter competences. The area with the lowest competence perception level was found to be foreign language. The teacher educators’ general subject matter competence perceptions were found to be not varying significantly depending on their gender, type of the university where they are working (state/foundation), academic title, discipline (educational sciences/subject area education) and teaching experience. In light of these findings, it can be argued that these competences should be considered in the recruitment of teacher educators in education faculties.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".