Problematic Approach to English Learning and Teaching: A Case in Indonesia
Bibliographic record
Abstract
This article deals with problematic approach to English learning and teaching due to misleading conception on the nature of English and on the process of acquiring it as well as the clues to the issues. The clues are: Firstly, English is not more difficult than any other languages, including Indonesian language, Bahasa Indonesia (Note 1). Secondly, there are two approaches that need considering in English instruction, grammar free and strict grammar approaches. The former is highly recommended for early age instruction and beginners whereas the latter is recommended for instruction for specific purposes. However the two approaches should collaborate and their applications should be based on needs analysis. Thirdly, Conflicting conception on whether L1 and L2 are the same processes should not deter the strategy of how language is acquired naturally. When proper conception on the nature of English is attained and it is approached properly, English subject is not burdensome and needs not be eliminated from Primary School curriculum, and there is no need to reduce time allotment for the subject in Senior High School as stipulated in Indonesian English curriculum amendment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".