Operationalizing Critical Literacy in EFL Classrooms in KSA: Possibilities and Challenges
Bibliographic record
Abstract
Using a qualitative research method, this article explores how EFL teachers can operationalize critical literacy (CL) when teaching prescribed textbooks that emphasize functional literacy in EFL classrooms in Kingdom of Saudi Arabia (KSA). To address this research issue, the action research demonstrated and reflected my experience of using CL in one of my college-level EFL classrooms. The study ends by shedding light on the possibilities and challenges which teachers may need to negotiate and navigate through when implementing CL in EFL classrooms. Data were collected from two primary sources: my teaching reflective journals and classroom observations. The results indicate that students’ engagement, autonomy, voice, agency, and critical conscious awareness can be maintained and promoted when CL is enacted in EFL classrooms. In addition, the findings show that the use of CL can empower students and teachers alike to take on new positions and play different roles in EFL classrooms. KSA can equip its learners with the necessary skills to critically engage with language and texts, empowering them to become informed, active participants in an increasingly globalized world.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".