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Grammar Teaching and Learning in L2: Necessary, but Boring?

2011· article· en· W2051046357 on OpenAlexaff
Gladys Jean, Daphnée Simard

Bibliographic record

VenueForeign Language Annals · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsGrammarCorrective feedbackPerceptionPsychologyLinguisticsMathematics education

Abstract

fetched live from OpenAlex

This descriptive inquiry-based study targeted second language (L2) high school students’ (n = 2321) and teachers’ (n = 45) beliefs and perceptions about grammar instruction, specifically about grammatical accuracy, corrective feedback, and diverse forms of grammar teaching and learning. Results showed only slight discrepancies between students’ and teachers’ beliefs and perceptions, and very few differences according to the target language and students’ gender or age. The main findings suggest that grammar instruction is perceived by both students and teachers as necessary and effective, but not as something they enjoy doing. Implications are discussed in view of the necessity to improve the teaching of L2 grammar, as students’ retention rate and motivation in L2 programs may be affected by the perceived as necessary but “oh so boring” learning of grammar!

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.272
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations180
Published2011
Admission routes1
Has abstractyes

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