ACTFL Standards and Their Implications to Taiwanese English Teacher Education and Professional Development
Bibliographic record
Abstract
In this globalized world, the growing importance of English learning has attracted more and more attention from researchers and educators, as the implementation of the new “English Lively Curriculum” in Taiwan demonstrates. The quality of English teachers is crucial in determining the quality of our English education, especially in an EFL environment like Taiwan. This study aims to examine the ACTFL Standards for the Preparation of Foreign Language Teachers in the US and their implications to English teacher education in Taiwan. Beginning with a review of foreign language teaching in the US, the ACTFL six new standards are introduced in detail. After this, the qualification processes and problems of the evaluation of English teacher education in Taiwan are discussed. Finally, by interviewing Taiwanese in-service English teachers, the possible implications of ACTFL standards to teacher education policy and institutions for Teacher Education in Taiwan are explored. Some of the implications include emphasizing the exposure to the ACTFL standards in Taiwanese teacher education programs and the need for a certificate from a well-organized English proficiency exam. In addition, the insufficient qualified English teacher and teaching resources in some rural districts should be improved by national policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".