My Self-Perspective as Future English Language Teacher Analysis of the Predictive Power of Mentoring Process
Bibliographic record
Abstract
The preparation of English language teachers to become beacons of global competitiveness necessitates teacher education institutions in the world to put emphasis on the mentoring process. The goal of this research was to assess the average degree of self-efficacy among English language preservice teachers and the variables that influence it. Over the course of the study, 150 third- and fourth-year English language student teachers (N=80/70; 90% females) were asked to fill out a self-report questionnaire about their perspectives and attitudes on the teaching profession. This information was gathered using the Mentoring for Effective Primary Teaching instrument and the Teachers' Sense of Efficacy Scale. Findings suggest that English language student teachers who have a mentor during their practicum experience are more likely to become effective English language educators. It was determined that there was a statistically significant correlation between the two variables. According to the findings, students had a much more positive view of their own teaching abilities in the fourth year compared to the third. The findings of this research suggest that the average level of confidence among English language preservice teachers varies considerably depending on whether or not one or both of their parents are also teachers. Multiple regression analysis shows that aspects of mentor teachers' teaching styles are the most reliable predictor of student teachers' evaluations of their own teaching abilities.
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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 | 0.004 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".