The Main Reason that Thailand's High School Students are Not Adapting in the English Language
Bibliographic record
Abstract
A large majority of high school students in Thailand have obstacles with the utilization of the English language; however, they have set their goal to be good at English. More than that, many students are intending and wanting to be even more successful in English. The objectives research regarding of a study of the main reasons why high school students in Thailand do not specialize in English were: (1) finding reasons they are not adapted and be good at English, (2) searching the reason why Thailand’s children defected of learning English languages, (3) providing plausible and reasonable solutions for students. The instrument used in this study was a questionnaire survey of 130 high school students from the various schools which information has been collected by using statistical analysis in terms of turning to be the percentage. Following, each of percentage values has been arranged into categories before finding the best solutions to assist the high school students. It follows that responses from Thai high school students who think they are average and below average are 55.4% and 23% respectively. As well as skills, Speaking is the weakest skill which has a variety of causes which half of responses are inexperienced; furthermore, the other answers such as being too afraid of speaking and unknowing vocabulary are factors which make inefficient improvement in speaking skill. Consequently, 49.2 percent of students think complexity of grammatical range and accuracy is difficult.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".