Implementing Critical Thinking Tasks to Fostering English Learners’ Intercultural Communicative Competence in a Genre-based Learning Environment
Bibliographic record
Abstract
The development of intercultural communicative competence in EFL (English as a Foreign Language) education in many countries is still a difficult goal to achieve. EFL teachers and learners require more tangible and concrete methodological approaches to foster this important competence in the classroom. Therefore, this reflection article aims at proposing the use of genre-based learning as a significant communicative language approach to foster English learners’ intercultural communicative competence (ICC) through a Sequence of Critical Thinking Tasks. Through two samples of genres, the article explains how the skills of discovery, of interpreting, and of relating, contained in the concept of ICC, can be articulated, complemented, and enhanced gradually through a set of more specific Critical Thinking Tasks. These mental skills can be useful to help learners understand, discover, interpret, and evaluate critically elements of deep culture that appear in different documents, genres, or texts produced by English-spoken cultures, other language communities, and learners’ own culture. Doing critical thinking tasks through genre-based approach can constitute a preliminary but significant step to enhance English learners’ critical intercultural awareness in EFL learning environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".