The Relationship between Prospective Teachers’ Thinking Styles and Attitudes towards Teaching Profession
Bibliographic record
Abstract
The aim of this study is to determine the prospective teachers' thinking styles, attitudes towards teaching professionand the relationship between thinking styles and attitudes towards teaching profession. Relational survey model wasused in the study. The universe of the study consists of the prospective teachers studying in the Faculty of Theology,Faculty of Theology and Pedagogical Formation Program of a state university in the fall semester of 2017-2018academic years. The sample of the study consisted of 1215 prospective teachers who were selected throughconvenience sampling method. According to the results of the study, prospective teachers preferred the mostlegislative, monarchic, executive, judicial, liberal thinking styles e.g. the hierarchic, conservative, oligarchic andanarchic thinking styles. Prospective teachers' attitudes towards teaching profession are positive. A significantpositive relationship was found between liberal, external, monarchic, executive, hierarchic, legislative, judicial andconservative thinking styles and attitudes towards teaching profession. On the other hand, a significant negativecorrelation was found between the oligarchic thinking style and the attitude towards teaching profession. Therelationship is moderate in liberal and external thinking styles and low in other thinking styles.
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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 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".