Attitude Behavior Tendencies and Knowledge Orientation as Antecedents of Maritime English Learning: Practical Implications for the International Maritime Industry
Bibliographic record
Abstract
Maritime English is a crucial element in the international maritime industry, enabling effective communication and ensuring safety at sea. Considering the importance of English proficiency in the maritime context, this study aims to develop an effective learning model using the Maritime English Reconstruction (MER) approach. This model is designed to enhance the effectiveness of maritime English learning through the application of relevant and contextual reconstruction techniques. The study examines the influence of attitude behavior tendencies, knowledge orientation, and the mediating role of MER on learning effectiveness. This research employs a quantitative method with data collection techniques through simple random sampling. The respondents in this study are final-year students majoring in maritime studies from various state universities in Jakarta and Semarang, Indonesia, with 248 valid questionnaires analyzed. Data analysis is conducted using Partial Least Squares-Structural Equation Modeling (PLS-SEM) with the assistance of SmartPLS 3 software. The results of the study indicate that attitude behavior tendencies and knowledge orientation have a significant influence on MER and learning effectiveness. Furthermore, MER is proven to act as a significant mediator in the relationship between attitude behavior tendencies and learning effectiveness, as well as between knowledge orientation and learning effectiveness. These findings suggest that the MER method plays a vital role in improving the results of maritime English learning by enhancing the application of relevant and contextual reconstruction techniques.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".