Developing Communication Strategies Instruction Used in an English as a Lingua Franca Academic Context
Bibliographic record
Abstract
This study examines the effects of communication strategies instruction on the ability to use English in an academic context. The participants were 28 students comprising 13 Thai students and 15 Cambodian students who enrolled at a Burapha university that used English as a lingua franca (ELF) in an academic context. The research instruments were pretest and posttest communication strategies tasks, communication strategies instruction, video recorder, observational field notes, and student reflections. Wilcoxon signed-rank test and coding method were used to analyze the data. The findings presented both quantitative and qualitative data. The quantitative data indicated that most of the students had higher scores after receiving communication strategies instruction. The qualitative data revealed that the students perceived better language use in the classroom after receiving communication strategies instruction. Moreover, they had more confidence to speak with their interlocutors. They also attempted to use communication strategies to help them overcome language difficulties. In addition, they had positive effects on this communication strategy instruction. The findings of this study suggest that English teachers play an important role in motivating low-proficiency students to speak English when implementing communication strategies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".