Language Training for EFL Teaching at the University of Toronto : Approaches and Teacher Roles
Bibliographic record
Abstract
Today, Japanese teachers of English as a foreign language (EFL) are involved in a large scale of educational reform.They are expected to develop their professional competence and autonomy by the Japan Ministry of Education, Culture, Science, and Technology (MEXT), which encourages them to implement Communicative Language Teaching (CLT) in their classes.This paper focuses on language training for EFL teaching at the University Toronto, and aims to examine how an instructor of the course (Instructor X) implemented CLT and what roles she played in the course.Sasaki, one of the authors, participated in the course as a student.We analyzed a variety of data, such as Sasaki's observations/ reflections, learning/teaching materials, Instructor X's teaching methods, the syllabus of the course, and the results of the questionnaires administered to the students and Instructor X.We recognized Instructor X's teaching principles and her effective approaches to the course, and considered some pedagogical implications for EFL teacher education in Japan.A teacher education course will be the product of the designers' and deliverers' educational philosophy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".