Grammar and Grammaring: Toward Modes for English Grammar Teaching in China
Bibliographic record
Abstract
The value of grammar instruction in foreign language learning and teaching has been a focus of debate for quite some time, which has resulted in different views on grammar and grammar teaching as well as different teaching approaches based on different perspectives or in different language learning contexts. To explore some modes for grammar teaching in China on the basis of distinguishing grammar and grammaring, this research reviews briefly the current situations of grammar teaching at colleges in China and the various teaching modes adopted in different teaching contexts. Two teaching modes are suggested, linguistic mode and story-telling mode, which may activate inquiry learning and active learning. Linguistic mode, which emphasizes the dual features of grammar learning, is more reasoning-centered than knowledge-centered and is designed from linguistic and academic perspective for advanced learners. Story telling mode, which focuses on smooth communication in different contexts, is more skill-centered than rule-centered and is designed from social and communicative perspective for beginners. Exploring the modes for teaching grammar from linguistic and social perspectives will be a pilot study for inquiring other aspects of grammar teaching as well as for teaching grammar to the learners of other languages.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".