Exploring the Role of English Literature in Developing Cultural Competence among ESL Students
Bibliographic record
Abstract
The study of English literature is a fascinating and effective technique for teaching English. It combines language instruction with literary analysis, contextualizing language, raising cultural awareness, honing critical thinking skills, expanding vocabulary and knowledge, boosting interpersonal skills, and enhancing writing ability. Teachers can enhance students' learning experience in English as a second language by employing these strategies: selecting appropriate texts, integrating reading practices, designing literacy exercises, assessing aesthetic aspects, and creating writing projects. The use of English literature in language studies fosters students' language development in relevant and real-world contexts, leading to a greater understanding of the language and its cultural nuances. Furthermore, culture affects values, beliefs, rituals, and behaviors and is reflected in language, dress, food, materials, and social institutions of a group (“Purnell, 2002”) qtd in (Sharifi et al., 2019). This understanding underscores the importance of integrating cultural elements into language instruction to provide students with a holistic view of language and its sociocultural context. Additionally, this approach allows us to examine how literature and language resources portray people of various origins, identities, demographics, and competences, seamlessly integrating diverse perspectives into the educational framework. This research involves investigating how a more varied educational environment affects student motivation, self-esteem, and the ability to communicate across cultures.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 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".