Integration of Literature in English Language Teaching: Learners’ Attitudes and Opinions
Bibliographic record
Abstract
Speaking of the role and position of literature in language teaching platforms, generally, two opposite views are in action, namely essentialist and non-essentialist. However, numerous studies have stressed the influential role of literature. This study investigated the opinions and preferences of EFL university students on the integration of literature in English language learning. To do so, a mixed-method research design was utilized in which data were collected quantitively through a 12 items questionnaire and qualitatively using five open-ended questions. The quantitative data were analysed using SPSS, whereas descriptions were used to analyse the qualitative data. For the quantitative part, 30 EFL students and for the qualitative phase, 10 EFL students participated. The findings indicated that students consider literature as a significant tool for learning English language and enhancing the four main language skills. It is also discovered that students find literary texts to develop awareness of cultural knowledge, which is inseparable in learning a second language. In a nutshell, the findings discovered that students showed positive attitudes toward using literature in learning English as a foreign language.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".