A Multiple Case Study of METI Cybersecurity Education and Training: A Basis for the Development of a Guiding Framework for Educational Approaches
Bibliographic record
Abstract
Cyberattacks have become a serious global concern, effecting enormous losses to different sectors. In the shipping business, major companies report violations to their operations’ integrity and security, and losing great amounts of money. While the International Maritime Organization (IMO), through the International Convention on Standards of Training, Certification and Watchkeeping for Seafarers (STCW) 1978, as amended, is yet to release a standard for the cybersecurity education and training of seafarers, some maritime education and training institutions (METIs) have acted proactively and included cybersecurity knowledge and skills in their curricular offerings. This study looked into the cybersecurity course offerings of four METIs that served as the case studies of the researchers. In particular, the following objectives were addressed: the cybersecurity knowledge and skills included in their curriculum; the importance of the cybersecurity knowledge and skills to seafarers; and the educational approaches of the METIs in delivering their topics on cybersecurity. The first and third objectives were answered using different sources of qualitative data, including document analysis, interview and direct observation. The quantitative approach, in the form of a survey questionnaire, was used to address the second objective. The METIs, though not the same in content, were found to have included cybersecurity knowledge and skills in their curriculum. These knowledge and skills were perceived to be very important by seafarers. Similar to the content of their courses, the METIs delivered their cybersecurity courses by employing varied educational approaches. To address the gap on the lack of cybersecurity course design and delivery minimum standards, a framework in the shape of a lantern is developed and proposed to guide maritime courses designers, in particular, and other course designers, in general.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".