The Implementation of the Mixed Techniques in Teaching English in Public Elementary Schools
Bibliographic record
Abstract
Kuwait, an Arabic-speaking country in which English serves important functions, has adopted so many methods in teaching EFL in public schools which in 1993 started with the Grammar-Translation and continued until 2005 the eclectic method was applied. In 2005 the emphasis on communicative techniques and almost entirely through listening and speaking in the first grade was used. This qualitative investigation of the opinions and teaching practices of twelve first grade teachers found that certain communicative techniques were seen by many of the teachers to contribute to slow academic progress and motivational problems: not translating vocabulary, not overtly correcting errors, not teaching reading and writing, and not giving formal tests. In spite of the important functions of the English language in Kuwait, it appeared that most of these first graders were not hearing English outside the classroom, which appears to be important for the success of the communicative method. Most teachers and some parents were concerned that the children were not being prepared for formal examinations in their future. The conversational frame of the drills and recitations probably contributed importantly to students’ understanding of English as a functional language. However, the communicative aim of encouraging students to absorb English through hearing it conversationally was undercut when the non-native-speaking teachers modeled English mistakes.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".