Use of Debate Strategies to Increase the Effectiveness of a 1st-Year Conversation Course at a College of Education
Bibliographic record
Abstract
This research aimed to identify the use of debate strategies in a first-year English as a Foreign Language (EFL) conversation course at the College of Basic Education in Kuwait with female students training to teach English. Debate activities’ extent and nature were examined to determine debate’s effect on students’ oral fluency, critical thinking skills, and self-confidence when speaking in public. Based on semi-structured interviews conducted with 27 students, the research outlined thematic areas regarding student attitudes, linguistic repertoires, and the difficulties arising from debate situations. The study showed that debate tactics improve students’ interest levels and thinking ability, especially those of the linguistically able. Regarding their learning experiences, many students said that they felt more at ease asserting themselves on public platforms and incorporating extra analytical skills while in debates. However, learners with poor L2 skills, especially students from public schools, had some dismal moments of participation—they even felt the debate activity excluded or challenged them.Nevertheless, the majority of the students acknowledged debate’s importance in enhancing fluency and communication skills. The findings similarly highlighted the need to adopt varied teaching methods in class depending on the learners’ language proficiency. They also suggested the addition of a higher-level debate class for advanced students so they may polish their interpersonal and analytical skills. These findings extended prior research on using debate as a pedagogical approach in teaching EFL classrooms and provided pedagogical implications for language education for teachers and policymakers in teacher education programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".