A Study of Communicative Strategies of Thai and Filipino Teachers of English
Bibliographic record
Abstract
There are many non-native English language teachers communicating with each other on a daily basis in English. The communicative strategies of non-native English language teachers can be easily identified. This study investigated the communicative strategies used by Thai and Filipino teachers. This research focused on the teacher’s interaction, the framework of communicative strategies of ASEAN English as a Lingua Franca (ELF) speaker, and the lack of studies in communicative strategies. In addition, the study aimed to investigate the significant relationship and communicative strategies between intercultural teaching personnel. The population sample consisted of Thai and Filipinos teachers who provide classroom instruction in English. The research tools used to collect data included a questionnaire, observations during two pair speaking tasks, and a Jigsaw task. A stimulated recall interview was performed after the tasks. All conversations and interactions were recorded and then transcribed. The results revealed that as listeners, “Listen to the message” was ranked the highest among the communicative strategies used by both the Thai and Filipino teachers. “Non-verbal language” was ranked the highest for the Thai teachers; while, “Persuasion” was most frequently used by the Filipino teachers. A Chi-square test showed that there was a statistically significant relationship between communicative strategies used by the Thai and Filipino teachers. Based on the findings of the study, communicative strategies identified in this study should be incorporated into English curriculums and English language teaching in Thailand. Educators, teachers, and non-native English learners should adopt these communicative strategies to promote mutual understandings in the ELF 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".