A Paradigm Shift of Learning in Maritime Education amidst COVID-19 Pandemic
Bibliographic record
Abstract
AbstractThis paper aimed to determine the maritime students’ readiness to cope with the abrupt paradigm shift from face-to-face to online learning for the first time in maritime education amidst the outbreak of the COVID-19 pandemic. It utilized a descriptive-normative approach where incoming 2nd year and 3rd-year maritime students were the respondents. Data gathering was online using a survey questionnaire in Google Forms. Statistical tools used were frequency count, percentage and a 5% margin of error in projecting students’ population throughout the schoolyear 2020-2021.The study showed a majority or 7 of 10 among maritime students were not ready to cope with the paradigm shift on the basis of not having a computer of their own for school works; no internet connectivity at home; no access to internet shops; and personal wellbeing. Almost 3 of 5 preferred face-to-face learning. Participation was limited with only the maritime students while everybody was under the community lockdowns for safety and health reasons. Internet connectivity of the students also was a limiting factor.A catch-up framework in maritime education for SY 2020-2021, 2021-2022, and 2022-2023 addresses problems relative to the start of SY 2020-2021 by delaying it to January 2021. This will give the maritime students inclusive opportunities to graduate within the prescribed period despite the impact of the COVID-19 pandemic. The government, school authorities, parents and faculty members also benefit from this framework once adapted.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".