Iranian EFL Teachers’ Perceptions of Task-Based Language Pedagogy
Bibliographic record
Abstract
Recently task-based language teaching (TBLT) evolving from communicative language instruction has drawn the attention of many researchers towards itself. To date, there have been few systematic studies on teachers’ perceptions in this field. This study has intended to explore teachers’ perceptions of task-based language pedagogy and the tasks used in the foreign language classrooms of Iran. It also seeks to investigate Iranian EFL teachers’ views on implementing TBLT and the reasons which make them choose or avoid implementing TBLT. A sample of 51 EFL teachers participated in this study. A questionnaire was used to examine the perceptions of the Iranian EFL teachers towards TBLT and the data were analyzed qualitatively and quantitatively. The results of the study showed that most participants understand TBLT concepts and principles very well and there are just a few negative views on the application of this approach in English classrooms of Iran. This implies that EFL teachers can be hopeful to successfully apply TBLT in their classes. Ultimately, it is believed that the results of such research will encourage EFL teachers to have more positive attitudes towards TBLT. Key words: Task-based Language Teaching (TBLT); EFL contexts; Teachers’ perceptions
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 | 0.003 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".