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Record W2130739749 · doi:10.5539/elt.v8n12p79

Grammar and Grammaring: Toward Modes for English Grammar Teaching in China

2015· article· en· W2130739749 on OpenAlexvenueno aff
Chengyu Nan

Bibliographic record

VenueEnglish Language Teaching · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
FundersPeople's Government of Jilin Province
KeywordsGrammarLinguisticsTraditional grammarEmergent grammarPerspective (graphical)Relational grammarTeaching methodComputer sciencePsychologyMathematics educationArtificial intelligence

Abstract

fetched live from OpenAlex

<p>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.</p>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.265
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2015
Admission routes1
Has abstractyes

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