Use of Communication Strategies in Oral Interactions: (In)effectiveness of CLT Approach in L2 Teaching
Bibliographic record
Abstract
Communication strategies (CS) have been generally used to overcome oral communication problems in delivering the intended messages. While studies on CS mostly involved simulated communication contexts, the current study deviates from the past studies on CS by identifying the types of CS employed by candidates of real job interviews and examining the effectiveness of Communicative Language Teaching (CLT) approach adopted in Malaysian schools in English language teaching. The data were collected from observations made on oral interactions between candidates and the panelists of academic staff recruitment interviews at one public university in the east coast of Malaysia. The recorded oral data were imported into NVivo software (version 12) before the use of CS by the candidates were categorised based on CS taxonomies proposed by Dörnyei and Scott, and Clennell. The results revealed that the candidates employed various types of CS with fillers and self-repetitions being the most frequently employed strategies while the two least employed strategies were asking for clarification and guessing. While the results showed extensive use of CS in the interactions, the high frequency use of fillers as a time-gaining strategy might reflect that the speakers lacked competency in conveying their messages. Additionally, too much use of fillers might not be favoured by the interlocutors since the strategy could occur unpredictably. The findings indicate that CLT approach has not been effective in enhancing English language competency among L2 learners. Considering this, intervention measures to improve the current situation by looking at the root problems in the implementation of CLT are needed if the government decides to retain this teaching approach in Malaysian schools.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".