Academic and real-life task-based language needs of marine engineering students: interface between students' and subject teachers' perspectives
Bibliographic record
Abstract
English for Specific Purposes (ESP) and Needs Analysis (NA) have been studied to a great extent, since a couple of decades ago. The review of the related studies also shows that needs analysis has been of much concern to the researchers interested in the ESP field. However, ESP for the students of marine engineering has not been investigated in terms of the task-based language needs. The researchers used a quantitative survey. To collect the data, a researcher developed questionnaire consisting of two components (academic & real-life) was employed. The data were analyzed through descriptive and inferential statistics (independent samples-t-tests). Both ME students and subject specialists believed that the academic and real-life task-based language needs are important to ME students. Results also showed that the differences between mean scores of the students and subject specialists were statistically significant. It can be concluded that maritime engineering students, to accomplish their study, need mastery in both receptive and productive language skills. Findings are both theoretically and pedagogically important to ESP educators, administrators of the universities as well the policymakers and administrators of marine engineering.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".