Debate: One of the Key Factors to Improving Students’ English Language Speaking Skills
Bibliographic record
Abstract
Over the last decade, English debates have become very popular in Cambodia. Most students with experience debating in English tend to have good English-speaking skills. It is interesting to learn more about the effect of debating in English on these students’ English language skills. Thus, this research paper aims to look into the debate's impact on the English language speaking skills of English as a Foreign Language (EFL) university students who have participated in debate competitions in Cambodia. It was a case study at The University of Cambodia (UC). The qualitative approach was used, and the total participants in the study were ten undergraduate students who participated in a debate competition in the English language in Phnom Penh, Cambodia. The study results showed that the respondents positively perceived a debate. They reported that debate improved their English language speaking skills. In conclusion, the debate has positively impacted my speaking performance and other critical thinking skills. Therefore, it should be added to the university's program as an extracurricular activity.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".