Challenges of simulation training for future engineering seafarers - A qualitative case study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Maritime transportation is currently in a transitional period to an impending autonomous future. To that end, novel technologies are increasingly being introduced on-board ships and their engine rooms. At the same time, advancements in digitalization and automation are progressively replacing and reducing the number of marine engineers on-board. Consequently, with increasing automation in machinery spaces and unmanned engine rooms, the role of the marine engineers has been altered to that of monitoring and oversight. The substantial changes in the nature of tools and job description of the marine engineers necessitate the re-assessment and revision of their training and pedagogy. Currently, the simulator is a powerful tool in the training and development of marine operators. Although the literature review reveals some interest in marine engineering simulation training, however, there is a lack of attention to remote and cloud-based simulation training as part of blended learning. This study reveals that imparting marine engineering simulation training online is not free from challenges. This study reports the findings from a qualitative study of marine engineering simulation training, conducted as part of a larger ethnographic study on developing maritime competence. The study utilizes the socio-historical, context-dependent framework of the Activity System (AS) to analyze marine engineering simulation training. The study reveals issues with cloud-based marine engineering simulation training. Firstly, cloud-based training is not seamless to access. Secondly, not all features present in the desktop simulation are present in the cloud version. Thirdly the cloud-based platform affords limited feedback in comparison to the desktop version. Fourthly, cloud-based simulation training does not support peer learning. An understanding of the challenges of cloud-based marine engineering simulation training will help address these concerns. Furthermore, it will facilitate the competence development of marine engineers as they work in increasingly automated workspaces in the transition to autonomous ship operations.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it