Survey Of Maritime Student Satisfaction: A Case Study On The International Student Survey To Identify The Satisfaction Of Students In Mathematical Courses
Bibliographic record
Abstract
This study presents the analyses of students’ preferences, satisfaction and perception of learning mathematical subjects at higher education maritime institutions in Croatia, Latvia, Estonia and Poland. All these institutions participate as project partners in the MareMathics project. In order to evaluate the effectiveness of teaching and learning mathematics, a preliminary student survey was conducted in all project partner institutions. Two indicators were analyzed: exam success rate and learning outcomes achieved. The developed online questionnaire contained a number of questions about the teaching methods and tools used by lecturers. Students assessed the impact of different teaching methods, expressed their satisfaction with learning materials, their impact on the results achieved by them, and the overall course. The analysis of the obtained data revealed that students faced difficulties in completing their tasks within subjects on mathematics and statistics. These research results lead to the conclusion that the used methods and tools of teaching mathematics and statistics, which are the essential influential factors on the overall satisfaction of students, are not effective and need to be modernized in the institutions under consideration. And this fact is crucial in the process of study and teaching of mathematical subjects as those subjects make up the base and necessary tools in learning other courses contained in the study program, especially in technical and engineering studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".