Learners’ Perception About and Development of Inter/multi-cultural Awareness Through Content and Language Integrated Learning (CLIL) of English Fiction in Bangladesh
Bibliographic record
Abstract
To teach literature in a second or foreign language classroom, different models are used worldwide—such as the Cultural Model, Language Model, and Personal Growth Model. Among these, the Cultural Model is commonly used in Bangladesh. However, it is usually taught through lecture-based methods, leaving little room for students to respond or show their cultural awareness. The Cultural Model connects with the "culture" aspect of the 4Cs framework (content, cognition, communication, and culture) in the Content and Language Integrated Learning (CLIL) approach. Applying CLIL in fiction teaching can create opportunities for active communication, cultural awareness, and appreciation of cultural diversity. This study used the 4Cs framework of CLIL and applied a mixed-method approach, collecting both quantitative data accrued through a questionnaire survey responded by 40 learners and qualitative data collected through interviews with 10 participants from undergraduate fiction classes, and secondary sources. The findings show that using CLIL in teaching fiction improved cultural awareness and communication among students. A task-based approach helped learners engage in classroom activities, which allowed them to practice awareness, tolerance, and acceptance of cultural diversity. Results indicate a clear improvement in students’ intercultural knowledge and their ability to respond empathetically and positively to cultural differences.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".