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Record W2617466135 · doi:10.5430/ijhe.v6n3p82

Development of Critical Thinking Skills through Writing Tasks: Challenges Facing Maritime English Students at Aqaba College, AlBalqa Applied University, Jordan

2017· article· en· W2617466135 on OpenAlexvenueno aff
Ali Odeh Hammoud Alidmat, Mohamed Ayed Ibrahim Ayassrah

Bibliographic record

VenueInternational Journal of Higher Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Mathematics educationCritical thinkingTask (project management)Foreign languagePerceptionPsychologyPedagogyEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.379
Teacher spread0.346 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2017
Admission routes1
Has abstractyes

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