The Use of Literary Texts in EFL Coursebooks: An Exploratory Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".