Exploring EFL Teachers’ Perceptions of CLIL and Its Implementation in the Saudi EFL Context
Bibliographic record
Abstract
This study aims to explore the perceptions of English as Foreign Language (EFL) teachers about the notion of Content and Language Integrated Learning (CLIL) in the Saudi EFL contexts. The veteran EFL teachers share their views regarding the application and implementation of CLIL approach. This study has looked into this issue from an interpretivist lens employing qualitative data collection technique, i. e. semi-structured interviews. The data were collected from 10 EFL teachers. The qualitative data were thematically analyzed that led to four major themes. The key findings reveal that CLIL is not a frequently used concept in this part of the world; however, its usefulness and effectiveness are recognized by the participants who also express their views about the pros and cons and other practical constraints that might affect the implementation of CLIL programs in the Saudi EFL context. One of the major issues that teachers have highlighted is the training programs that should encompass the idea of CLIL and train EFL teachers on the latest teaching skills to upgrade their pedagogical repertoire. Based on the findings, the study has suggested directions for the future researchers to fill the gap in the existing literature and investigate the phenomenon of CLIL in more depth in order to acknowledge its significance in the light of the Vision 2030.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".