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Record W3064653529 · doi:10.5539/ells.v10n3p86

The Use of Literary Texts in EFL Coursebooks: An Exploratory Study

2020· article· en· W3064653529 on OpenAlexvenueno aff
Shaima J. Al-Saeed, Abdullah Alenezi

Bibliographic record

VenueEnglish Language and Literature Studies · 2020
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsExploratory researchLinguisticsEnglish as a foreign languageSubject (documents)Selection (genetic algorithm)Applied linguisticsPsychologySociologyLiteratureComputer scienceArtPhilosophyLibrary scienceSocial scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This exploratory study investigates the use of literary texts in English as a foreign language (EFL) coursebooks and examines the extent to which literature is used within the coursebooks, the types of texts used as regards authenticity and recency, the criteria for selecting and adapting the texts and the ways of improving the selection and adaptation process. Multiple articles written on this subject show that the evaluation of EFL coursebooks is a relevant and important research area in the study of language and linguistics. This study gives a survey of the extent to which literary texts are used in EFL coursebooks within institutions of higher learning in Kuwait and worldwide. In this study, 44 popular EFL coursebooks (between 2015 and 2019) within higher education institutes, including those in Kuwait, were analysed. The findings demonstrated that literary texts are not included in many of the coursebooks used nowadays and that the literary texts selected were primarily from an early period (more than a century ago). Furthermore, the results revealed that the coursebooks include a large percentage of inauthentic, ill-adapted works. Consequently, this study recommends incorporating authentic literary texts in EFL coursebooks comprising modern literature.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.473

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)0.0000.000

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 head0.040
GPT teacher head0.327
Teacher spread0.286 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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