The Effectiveness of Teaching Formulaic Politeness Strategies in Making Request to Undergraduates in an ESL Classroom
Bibliographic record
Abstract
It is widely acknowledged that the main thrust of second language (L2) teaching and learning is establishing and developing the communicative competence of learners. Especially, in recent years, the focus has shifted more towards intercultural communicative competence (ICC). As such, it is more practical that educational endeavors should be directed both towards the grammar or lexis of the target language as well as the appropriate use of these grammatical and lexical systems in a variety of situations by considering different social and contextual factors. Therefore, this study embarks on the effect of explicit instruction of formulaic politeness strategies among Malaysian undergraduates in making request. Sixty Malaysian undergraduates participated in the study. The students included two groups of intervention and control groups. The data were cumulated through three tests, namely open ended completion test, a listening test and an acceptability judgment test. Treatment or experimental group received explicit instruction with structured and problem-solving and input tasks. The comparison was made between the performance of treatment group and that of control in terms of the pre-test and post-test. The findings show that the treatment group outperformed significantly than the control group. This matter is suggestive that in this probe, explicit form-based instruction was successful for learners to comprehend and produce the English politeness strategies effectively in making request. The findings of this study will be beneficial for material developers and teachers to make use of form-focused strategies more effectively to teach second language pragmatic features to Malaysian students.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".