Improving EFL Learners’ Critical Thinking Skills in Argumentative Writing
Bibliographic record
Abstract
In the 21st century where information has become easily available and accessible, education has shifted its attention to teaching students how to process and think critically about the information they receive. Welcoming the changes that education constantly witnesses, the field of English Language Teaching (ELT) has embraced the integration of critical thinking. Accordingly, the present paper aims to explore the effect, if any, of integrating critical thinking on learners’ use of critical thinking skills in argumentative writing. To this end, an experimental study was conducted; 36 Moroccan EFL learners from the department of English were divided evenly into an experimental group and a control group. While the participants in the experimental group were taught writing with critical thinking skills, the others were taught writing with no reference to these skills. The participants in both groups took a pre-test and posttest to evaluate the development of their use of critical thinking skills in argumentative writing. The data which has been quantitatively analyzed indicates that the experimental group significantly outperformed the control group. The students’ ability to use more credible evidence, address alternative arguments, support conclusions, and maintain the logical flow of ideas in their essays did not reach a mastery level in the posttest, yet the average level they reached is reassuring in view of the short time of the training they had. An integration of CT for longer periods may bring forth encouraging outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".