Development of Critical Thinking Skills through Writing Tasks: Challenges Facing Maritime English Students at Aqaba College, AlBalqa Applied University, Jordan
Bibliographic record
Abstract
Teaching English for Special Purposes (ESP) in a context where English is taught as a Foreign Language (EFL) is no easy task. There is in fact extensive research reporting on challenges facing both teacher and student in the Foreign Language classroom where language skills must be learnt outside their usual context. Even more challenging is teaching or learning a conceptual skill like critical thinking through writing in an EFL context. The objective of this paper is to identify and describe writing tasks contained in the ESP programme with a view to examine the correspondence between the tasks and the critical thinking skills. To this end, the study examines self-reported perceptions, experiences and opinions by Maritime English students of Aqaba College in Jordan who take an ESP course and who are supposed to develop their critical thinking skills through carefully selected writing tasks in English. The study applies the qualitative procedure of in-depth interview and explores a sample of 10 finalist undergraduate informants on issues related to their writing tasks in English. Findings of the study revealed, among other things, that there is low correspondence between writing tasks contained in the ESP programme and critical thinking skills, and that writing tasks featured in the programme pursue more mechanical writing than thinking.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".