Developing Critical Thinking Skills Through English Writing Assignments at King Faisal University
Bibliographic record
Abstract
Developing critical thinking skills in English as a Foreign Language (EFL) learners remains a persistent challenge in higher education, particularly in contexts where writing instruction focuses primarily on language accuracy rather than analytical depth. In Saudi Arabia, university writing courses often emphasize grammar, vocabulary, and formal structure, leaving limited space for fostering students’ reasoning, evaluation, and argumentation skills. This gap in instructional design results in graduates who may be linguistically competent but lack the higher-order thinking abilities needed for academic success and real-world problem-solving. While global research acknowledges the strong relationship between writing and critical thinking development, few studies have examined how culturally relevant and pedagogically structured writing assignments can enhance critical thinking in Saudi EFL classrooms. This study addresses this gap by investigating the effectiveness of integrating explicit critical thinking strategies, such as argumentative and problem-solution essays, peer feedback, and technology-supported revision, into English writing instruction at King Faisal University (KFU). The research targeted 80 undergraduate engineering students aged 19–23, divided into experimental and control groups, over a 16-week semester. Using a mixed-methods design, the study measured students’ improvement through pre- and post-tests, rubric-based writing evaluations, and semi-structured interviews. The findings revealed that students in the experimental group showed significant gains in critical thinking performance, writing quality, and engagement compared to those in the control group. These results suggest that intentional instructional design—grounded in culturally meaningful topics and collaborative learning practices—can transform writing courses into platforms for developing both language and cognitive skills. The implications of this research extend beyond the classroom, offering practical insights for EFL instructors, curriculum designers, and educational policymakers seeking to promote 21st-century competencies. While the study’s scope was limited by sample size and duration, it lays a foundation for broader implementation and future research into sustained, large-scale interventions in similar EFL contexts.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".