The Effects of Form-Focused Communicative Grammar Instruction on Students’ Pronunciation and Grammar in Speaking
Bibliographic record
Abstract
Integrating form-focused instruction (FFI) in the communicative language class is the most recently used language teaching and learning approach today. To draw learners’ attention to grammatical form, FFI implements different types of corrective feedback and implicit and explicit grammar teaching techniques. This study investigated the effects of FFI combined to communicative grammar instruction on students’ pronunciation and grammar in speaking. The main objective of this study was to probe whether the form-focused instruction integrated with communicative grammar activities was effective in terms of students’ performance of pronunciation and grammar in speaking. The study employed a quasi-experimental study design with a mixed method approach. It also employed pre and post-tests, questionnaire, and interview in order to gather data. The data collected by the tests and questionnaire were analyzed quantitatively using independent and paired sample t-test where as the interview data was analyzed thematically. The results of the study revealed that the experimental group of students who had been taught spoken English having form-focused communicative grammar instruction showed the enhancement on pronunciation and grammar in speaking in their post-test scores. This directed to the conclusion that the form-focused communicative grammar instruction assisted the students advance their pronunciation and grammar in speaking. Thus, it is recommended that high school EFL teachers should implement communicative grammar activities integrated to form-focused instruction while teaching speaking skills.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".