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Record W2803625219 · doi:10.5539/ells.v8n2p63

ELT in Algeria: The Hegemony of the Teach-to-the-Test Approach

2018· article· en· W2803625219 on OpenAlexvenueno aff
Smail Benmoussat, Nabil Djawad Benmoussat

Bibliographic record

VenueEnglish Language and Literature Studies · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsPaceTest (biology)Set (abstract data type)HegemonyCommunicative language teachingMathematics educationSWORDPreferenceForeign languageNothingLanguage educationLinguisticsPsychologyPedagogyComputer sciencePolitical scienceEpistemologyPhilosophyMathematics

Abstract

fetched live from OpenAlex

The late 1970s witnessed the emergence of a widespread movement that expressed its reaction against the approaches and methods which focused too much on the teaching of discrete items. That was clearly stated by applied linguists, teachers and educators who virtually all contended that traditional methods which used translation and systematic grammatical analysis left the language learners little time to practice the spoken language and to enhance their communicative abilities. As a direct outcome of such reaction, a concern developed to make foreign language teaching, not least English “communicative”. Communicative Language Teaching, henceforth CLT, has attracted a worldwide interest. Regrettably, seldom is testing processed “communicatively”; most Algerian EFL teachers prefer to cling tenaciously to the teach-to-the-test approach “principles”. This preference is closely related to the notion of “achievement” which means nothing more than giving the opportunity to the learner to score well on standardized tests and high-stakes exams. This dimension indicates the extent to which the teach-to-the-test approach acts as the “sword of Damocles” hanging over ELT in Algeria converting EFL keep-pace learners into set-the-pace swots.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.235
Teacher spread0.225 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2018
Admission routes1
Has abstractyes

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