Materials Design in Language Teacher Education: An Example from Southeast Asia
Bibliographic record
Abstract
This chapter describes an approach that has been developed to induct language teachers into the principles and practices involved in writing course materials for use in countries that are members of SEAMEO — the Southeast Asian Ministers of Education Organization. SEAMEO hosts a number of centres in member countries, each with a particular focus and mandate. The SEAMEO centre in Singapore is under the auspices of the Singapore Ministry of Education and is known as the Regional Language Centre (RELC). Among the courses RELC provides to teachers and teacher educators from the ten SEAMEO member countries are in-services courses and workshops on topics such as CLIL, ESP, and English for Young Learners, as well as courses linked to postgraduate qualifications, taught in both face-to-face and blended formats. In its earlier years RELC lecturers were sponsored by both Singapore as well by member or associate-member countries and I was the New Zealand Government staff member on two occasions. More recently I have been an adjunct professor at RELC, visiting RELC annually to teach courses and workshops on curriculum and materials design. This paper describes an approach I have developed while working with course participants in this capacity. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".