Examining the use of blended learning in maritime education and training
Bibliographic record
Abstract
Nowadays, Maritime Education and Training (MET) is seen as a significant aspect in improving seafarers' understanding, knowledge, and proficiency under the International Convention on Standards of Training, Certification, and Watchkeeping for Seafarers (STCW).However, this paradigm faces many challenges.To solve the issues, METIs are trying to develop Blended Learning (BL) approach.This dissertation tried to identify the modality of BL by literature review, which describes how BL can cope with the limitations of the current MET paradigm.It also looked at the current status, limitations, and the effectiveness of collaboration among Maritime Education & Training Institutions (METIs) to improve learning programs concerning BL by conducting interviews.Two strategies were used in this research further to disseminate BL: a literature review and semi-structured interviews.Findings from the literature revealed that BL has four characteristics composed of net-centricity, which means students can take lectures whenever and wherever they are, tailored syllabus, accurate assessment, and enhanced interaction.All of these elements can compensate for limitations competence-based training.Effective BL is based on pre-defined legal sources, highly developed technical infrastructure, and well-trained human resources.The interview results indicate that the pandemic of COVID-19 has accelerated institutions explored to adopt BL and this trend.However, modality, except for netcentricity, is not observed from the interview.This might be because they were forced to rely only on e-learning.The analysis of the interview results also revealed that several METIs lack legal, technical, and human resource basis.As a result, a legal basis for BL, such as guidance, should be developed at IMO.Furthermore, some institutions suffer from unstable internet connections in terms of technical infrastructure, so alternative measures, such as satellite communication, should be considered.Moreover, in terms of human resources, only a few institutions provide BL training for instructors.Instead, institutions have sought to improve their BL by providing webinars for instructors, weekly meetings with faculty members, peer learning, and knowledge sharing sessions on how to conduct BL courses online.Finally, findings revealed that collaboration could save money and enable METIs to deliver enhanced and improved training programs by sharing facilities and human resources.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".