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Record W2906218402 · doi:10.5539/elt.v12n1p169

Foreign Language Teaching in Over-Crowded Classes

2018· article· en· W2906218402 on OpenAlexvenueno aff
Halil Küçükler, Abdullah Kodal

Bibliographic record

VenueEnglish Language Teaching · 2018
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsForeign languageLanguage assessmentLanguage educationCurriculumClass (philosophy)Comprehension approachTeaching methodLanguage transferMathematics educationCommunicative language teachingPsychologyPedagogyEnglish languageTest of English as a Foreign LanguageLanguage industryLanguage pedagogyComputer science

Abstract

fetched live from OpenAlex

The importance of English in Foreign Language learning has been widely accepted in recent years and the English language is now well established as an international language. There is a growing significance of foreign language in education. As English has been widely used internationally, many people are interested in English and prefer learning English. When it is considered in public schools, English teaching has become more intense in school curricula. There are many barriers in language teaching in from primary education to higher education. One of the most important barriers in foreign language teaching is crowded especially over crowded classes. In crowded classes, classroom management, getting results from language approaches becomes difficult. In addition to this, a small number of class hours per week is another barrier in language teaching. The purpose of this study is to examine this issue and to examine the question of how language teaching is handled in these crowded classes and what different activities are useful to apply. If the educators are unable to change the classroom order, what are the appropriate language activities and how to apply them.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.010
GPT teacher head0.324
Teacher spread0.314 · 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 designObservational
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

Citations14
Published2018
Admission routes1
Has abstractyes

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