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\neducational reform. They are expected to develop their professional competence and autonomy by the\nJapan Ministry of Education, Culture, Science, and Technology (MEXT), which encourages them to\nimplement Communicative Language Teaching (CLT) in their classes. This paper focuses on language\ntraining for EFL teaching at the University Toronto, and aims to examine how an instructor of the\ncourse (Instructor X) implemented CLT and what roles she played in the course. Sasaki, one of the\nauthors, participated in the course as a student. We analyzed a variety of data, such as Sasaki’s observations/\nreflections, learning/teaching materials, Instructor X’s teaching methods, the syllabus of the course, and\nthe results of the questionnaires administered to the students and Instructor X. We recognized Instructor\nX’s teaching principles and her effective approaches to the course, and considered some pedagogical\nimplications for EFL teacher education in Japan. A teacher education course will be the product of the\ndesigners' and deliverers' educational philosophy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".