Enhancing Thai-English Translation Capabilities of EFL Undergraduates with the Principle of Haujai Nakpraj (Learned-man Approach)
Bibliographic record
Abstract
Knowledge of subject-verb agreement (concord) plays a vital role in Thai-English translation by EFL undergraduates because the knowledge foundation of concord is essential for Thai-English translation. Complicated rules of concord cause problems for EFL undergraduates’ when it comes to Thai-English translation. Their knowledge can be developed and improved utilizing a specific Thai learning style called Haujai Nakpraj Learning Style (HNLS) which means “Learned-man Approach or The Heart of the Learned man”. This pre-experimental preliminary research used a one-group pre-test and post-test design study aiming at improving the knowledge of concord. It hopes to achieve this through the activities designed based on the principle of the Haujai Nakpraj Learning Style (HNLS). The goal is to enhance the ability of nine EFL Northern Thai undergraduate students in Thai-English translation as a sample group enrolled in the Introduction to Translation Course during the summer semester of the 2020 academic year in comparison to the Thai-English translation ability of EFL Thai undergraduates.Data collection instruments consisted of pre-test and post-test with 62 items divided into two sections. The first 32 items concentrate on testing subject-verb agreement knowledge in addition to 30 multiple-choice items testing the Thai-English translation ability of the sample group together with threeThai-English translation subjective tests as qualitative data. The statistics used in the data analysis was the Wilcoxon Signed-Rank Test employed to compare the average scores of the target group’s pre-test and post-test. The findings revealed that the average score of the post-test on knowledge of subject-verb agreement in Thai-English translation was significantly higher than the average score of the pre-test at 0.05 level. In qualitative data analysis, findings revealed the sample group received high and average translation marks, with scores ranging from 16 to 20 for high, 11 to 15 for average, and 5 to 10 for low.This result suggested that the activities are effective to a certain extent as they can be applied for EFL learners in the translation source language (SL) to the target language (TL) 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".