Online distance learning (odl) in institutions of higher maritime education: advantages and disadvantages
Bibliographic record
Abstract
The article presents the main disadvantages and advantages of distance learning in educational institutions. Special attention is paid to the scientific developments of American and Canadian scientists regarding this problem. The author has characterized the main aspects of the influence of computer equipment on the quality of acquired knowledge during distance learning. Proficiency in maritime education can only be achieved by providing the most effective types of delivery models including online internet based distance learning. If those online methods are recognized as effective and productive, simulator and web based learning should be considered acceptable and recognised means of instruction for the majority of the subjects in the maritime education. But before jumping into these conclusions we need to determine whether Online Distance Learning (ODL) would be appropriate to the students, especially to maritime students, whether it has any limitations that would prevent its use from teaching any particular subjects. Or whether advantages of using it would outweigh disadvantages. In this article, we will review recent research studies and articles on this topic with the goal to determine how effective is o.nline delivery model versus traditional face to face teaching in different learning settings with the particular focus on its implementation in vocational education. Due to the Pandemic and martial law in Ukraine educational institutions are look.ing toward on.line learning pl.atforms to continue with the process of educating s.tudents. Online lear.ning is now app.licable not just to le.arn acade.mics but it also ex.tends to learning extracu.rricular activ.ities for students as well. The author indicated that both online and blended models of teaching have their disadvantages. The author noted thatl, those methods could create a sense of isolation, and may be not very useful for the students who are progressing while communicating face to face in the traditional classroom environment, who like socializing and not to be isolated at home.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".