Approaches and Benefits of Teaching English through Literature Curriculum at Myanmar Universities: Insights from Stakeholders
Bibliographic record
Abstract
The curriculum serves as a fundamental bridge between teachers and students, fostering an effective learning environment. This qualitative research investigates the pedagogical approaches employed by English literature teachers and evaluates the perceived benefits of using an English literature curriculum in EFL classrooms of Myanmar. The study involved 27 English literature teachers, six government officials, and three local business leaders through interviews, classroom observations, and focus group discussions. Four primary teaching approaches were identified: Paraphrastic, Information-Based, Language-Based, and Integrated Approaches. These methodologies are specifically designed to meet the diverse needs of students, thereby enriching their educational experiences. The research employed purposive sampling to select participants and conducted thematic analysis on the collected data through content and document analysis. The findings underscore the significance of integrating various literature genres, such as prose fiction, short stories, novels, poetry, and drama, into language education. This integration not only enhances language proficiency but also fosters critical thinking and cultural awareness among students. The study highlights the importance of a multifaceted approach to teaching literature, which can cater to different learning styles and preferences. To advance research in EFL education and pedagogy, future studies should focus on investigating specific pedagogical approaches, assessing the impact of digital technologies, and conducting longitudinal studies to deepen the understanding of literature’s role in language learning. The insights from this study provide valuable implications for curriculum developers, educators, and policymakers in enhancing the effectiveness of literature-based language education.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".