Effects of Content-Based Instruction on English Language Performance of Thai Undergraduate Students in a Non-English Program
Bibliographic record
Abstract
The research purposes were to: 1) develop lesson plans for content-based instruction; 2) evaluate the students’ perceptions of the effectiveness of content-based instruction; and 3) investigate the effects of content-based instruction on English language performance of Thai undergraduate students in a non-English program. The sample group was 19 Thai undergraduate students. The research instruments were: 1) lesson plans for content-based instruction; 2) an evaluation form of lesson plans; 3) an effectiveness questionnaire on content-based instruction; and 4) English language performance tests. Data were analysed the mean, standard deviation, content analysis and a t-test. The research results were: 1) the lesson plans were developed and evaluated by experts as applicable for use at a high level and pilot-tested with 14 non-targeted Thai undergraduate students with the perceived effectiveness at a high level; 2) the 19 targeted Thai undergraduate students perceived the content-based instruction as an effective methodology and essential aid in generating opportunities to use English at a high level. They thought that it was fun and helped them practice, have a better attitude and gain more courage to express themselves in English; and 3) the post-course English language performance were significantly (P < 0.05) higher than the pre-course English language performance. In conclusion, content-based instruction produced positive results and could be used as an effective methodology and essential aid in generating opportunities to use English, which resulted in increased English language performance.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".