<scp>TESOL</scp> Teacher Education: Novice Teachers' Perceptions of Their Preparedness and Efficacy in the Classroom
Bibliographic record
Abstract
This study examined the teacher education of novice teachers of English to speakers of other languages (ESOL). A survey and follow‐up interviews were employed to investigate novice teachers' perceptions about four aspects of their teacher preparation: (a) degree of preparedness to teach after graduating from a teaching English to speakers of other languages (TESOL) program, (b) preparedness after classroom experience (up to 3 years), (c) sense of efficacy to complete teaching practices in adult ESOL classrooms, and (d) perceptions of what was useful to them in the TESOL program. Accredited ESOL teachers with less than 3 years of experience (N = 115) completed a questionnaire that explored their perceptions of preparedness and efficacy to teach in adult ESOL programs in Ontario, Canada. Eight teachers participated in follow‐up semistructured interviews. Findings show that although, overall, novice teachers increased their perceptions of preparedness by gaining experience in the classroom, their sense of efficacy to perform within certain teaching expectations was task specific and highly situated. The practicum and “real” teaching experiences were found to be the most influential aspects of the induction programs. These findings have implications for teacher educators, TESOL institutions, and accreditation bodies that are committed to preparing qualified teachers for adult ESOL programs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".