The role of grammar instruction in second language learning and teaching
Bibliographic record
Abstract
Does instruction make a difference? Is there an effective pedagogical intervention to grammar instruction? In the last fifty years, scholars have debated to what extent grammar instruction makes a difference in acquisition of morphological and syntactic aspects of language (VanPatten and Benati, 2015; Benati, Laval, Arche, 2013). Theory and research around the role of grammar instruction seem to indicate that grammar instruction might have a beneficial role in speeding up the rate of acquisition of formal properties of language. Despite the fact that language learners bring to the task of acquisition a variety of mechanisms that override instructional efforts, a type of instruction that is both input oriented and meaning-based might have a facilitative role in second language acquisition (Nassanji and Fotos, 2011; Benati, 2014). \n \nIn this paper, research findings on a number of pedagogical interventions (e.g., input flood, textual enhancement, structured input, structured output tasks) will be reviewed. Although, the findings are not completely conclusive on whether these instructional interventions have an impact on acquisition, it is clear that we have witnessed to a shift in the field from the original question “Does instruction make a difference?” to the more specific question “Does manipulating input make a difference?” \n \nIs there an effective pedagogical intervention to grammar instruction? The answer to this question is that there is not one particular type of instructional intervention better than others. However, it must be emphasised that effective types of grammar instruction share common and essential ingredients: (i) input plays a key role; (ii) input is manipulated so to facilitate input processing and grammar acquisition; (iii) grammar instruction should focus on both form and meaning; (iv) output grammar practice should follow input grammar practice. \n \nGrammar instruction should be less about the teaching of rules and more about exposure to form. It ought to be less about manipulating output and more about manipulating and processing input.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".