Incorporating Controversial Issues in Critical Thinking Lesson: A Case Study of EFL Classroom
Bibliographic record
Abstract
This case study investigated the issue of incorporating controversial topics in EFL classroom of pre-university students and their attitude toward such controversial topics namely, E-stalking in terms of their own cultural background. The objective of the study was to bring a new dimension in language teaching and learning in Bangladeshi educational system by introducing controversial issues in critical thinking lesson plan. Bangladeshi educational system is solely dependent on the grammar-translation method where including the controversial issue in a language classroom is a completely new experiment for both the teacher and learners. BRAC Institute of Language (BIL) of BRAC University is trying to come out from this traditional teacher oriented methods by implementing new modern approaches and techniques for language learning. This case study was a part of teaching critical thinking, aiming to improve their English language learning. The rationale that the study adopts for introducing controversial topics in EFL curricula is based on the assumption that it helps learners in developing their linguistic and cognitive skills, social awareness, emotional well-being and critical thinking that are compatible with the new approaches and methods of teaching EFL. The present study analyzed data that was collected from the students of pre-university (pre-intermediate level) program and found that majority of the students welcomed the topic E-stalking and showed their positive attitude throughout the lesson. This paper also revealed the importance and possible challenges of incorporating controversial topics in EFL classroom. This study ended with some recommendations made by the researcher’s own observation while conducting the lesson.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".