Promoting Reflective Practice in Initial English Language Teacher Education: Reflective Microteaching
Bibliographic record
Abstract
Reflective practice is based on the belief that teachers can improve their understanding of their own teaching by consciously reflecting on their teaching experiences. One way of promoting such reflective practice among preservice language teachers is to have them engage in reflective microteaching. Microteaching has as its main purpose the practice of specific skills of teaching during short simulated lessons. Over the years within the field of Education, microteaching has evolved from a training view of teacher education to a reflective approach, and this relatively new stance has been reviewed as having a favorable impact on the development of preservice teachers' teaching skills. However, within the field of TESOL, although there has been agreement that this reflective microteaching is desirable, not many studies have been reported on the impact of this reflective approach to microteaching. The purpose of this paper is to explore the impact of a reflective microteaching assignment in an initial English language teacher education program. Results indicate that if the purpose and requirements of the microteaching assignment are clearly articulated to the preservice teachers, they can have a positive impact, both real and perceived, on the development of English language teachers.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".