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Record W2982385185 · doi:10.5430/ijhe.v8n7p44

Teaching Grammar to Bilingual Learners

2019· article· en· W2982385185 on OpenAlexvenueno aff
Olga A. Bezuglova, Liliya Ilyasova, Zhanargul A. Beisembayeva

Bibliographic record

VenueInternational Journal of Higher Education · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
FundersKazan Federal University
KeywordsGrammarProcess (computing)Computer scienceCreativityFocus (optics)Mathematics educationObject (grammar)Action (physics)Subject (documents)PsychologyPedagogyLinguisticsArtificial intelligenceWorld Wide Web

Abstract

fetched live from OpenAlex

This article deals with an inductive way of English language teaching. This acquisition process can help bilingual students to learn, find rules and apply them to new contexts. The objective of the paper is to propose a model of teaching that promotes student-centered approach where a teacher guides learners in discovery and provides more opportunities to practice, particularly in grammar. Basis of such approach is a training environment, in which the student is not an object to whom knowledge is transferred in a ready-made form but the subject of training process therefore knowledge, abilities, skills for the student are the result of his researches, decisions and creativity. The model of teaching and student-centered activities has been explored through the action research. Based on the experiment, it can be emphasized that a progressive way of teaching grammar moves the focus away from the teacher as the information provider and enables students to focus on use. Finally, the inductive approach as more effective for achieving learning goals and outcomes, is presented in the article from the perspectives of the bilingual learners.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.963
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.015
GPT teacher head0.307
Teacher spread0.292 · 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 designNot applicable
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

Citations3
Published2019
Admission routes1
Has abstractyes

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