An Investigation of the Variables Predicting Faculty of Education Students’ Speaking Anxiety through Ordinal Logistic Regression Analysis
Bibliographic record
Abstract
The purpose of this study is to determine whether Cumhuriyet University Faculty of Education students’ levels of speaking anxiety are predicted by the variables of gender, department, grade, such sub-dimensions of “Speaking Self-Efficacy Scale for Pre-Service Teachers” as “public speaking”, “effective speaking”, “applying the speaking rules”, “organizing the speech content”, and “evaluating the speech”. Correlational survey model is employed in the study. While the dependent variable of the study is students’ “speaking anxiety”, its independent variables are gender, department, grade, and such sub-dimensions of “Speaking Self-Efficacy Scale for Pre-Service Teachers” as “public speaking”, “effective speaking”, “applying the speaking rules”, “organizing the speech content”, and “evaluating the speech”. The research population consists of 2983 students studying at seven departments of Faculty of Education of Cumhuriyet University in the 2015-2016 academic year. The research sample, on the other hand, is composed of 1057 students from seven departments of Faculty of Education of Cumhuriyet University. Data were collected via “Scale of Speaking Anxiety for Prospective Teachers”, which was developed by Kınay and Özkan (2014) to determine pre-service teachers’ speaking anxiety, and “Speaking Self-Efficacy Scale for Pre-Service Teachers”, which was developed by Katrancı and Melanlıoğlu (2013) to determine pre-service teachers’ speaking self-efficacy. Data were collected through ordinal logistic regression analysis as the dependent variable was made three-category and ordinal through cluster analysis. According to the logistic regression analysis results, gender, department, such sub-dimensions of “Speaking Self-Efficacy Scale for Pre-Service Teachers” as “public speaking” and “applying the speaking rules” have a significant influence on speaking anxiety.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".