Language Practitioners’ Reflections on Method-based and Post-method Pedagogies
Bibliographic record
Abstract
Method-based pedagogies are commonly applied in teaching English as a foreign language all over the world. However, in the last quarter of the 20th century, the concept of such pedagogies based on the application of a single best method in EFL started to be viewed with concerns by some scholars. In response to the growing concern against the concept of a method, some scholars started to offer alternatives to a method in different forms. Kumaravadivelu is one of the scholars who offers his post-method macro-strategic framework as an alternative to method-based pedagogies. This small-scale study explores English language practitioners’ experience and their views about applying method-based and post-method pedagogies. Semi-structured pre- and post-interviews were conducted from eight participants. The pre-interviews investigated the teacher-participants’ views about the method-based pedagogies in practice and the post-interviews aimed at knowing the prospects and concerns in the application of post-method pedagogies in their context. Although participants were skeptical of the concept of methods, they considered them useful in making contribution towards learning and teaching English. They found post-method pedagogies as more preferable option to method-based pedagogies in ELT on the ground; the post-method pedagogies, according to them, give broad directions while specific methods make teachers to work within narrow guidelines. However, they showed certain concerns in the application of such pedagogies in their context.
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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 | 0.152 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.040 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.010 | 0.023 |
| Insufficient payload (model declined to judge) | 0.006 | 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".