Evaluation of the influence of an adaptive instructional system on participants’ performance in a ship’s bridge simulator
Bibliographic record
Abstract
Effective ice management training is necessary for safe and efficient operations in sea ice environments, especially for offshore energy industries that experience seasonal incursions of pack and multi-year ice. Traditional training methods in sea ice management are predominantly through classroom courses, simulator-based training, and experiential learning on-the-job. However, traditional forms of training are non-adaptive, have limited scalability, and lack consistency in skill acquisition. This study evaluates the effectiveness of an Adaptive Instructional System (AIS) as a potential solution for improving ice management performance in simulation-based training, addressing a gap by providing adaptive, tailored feedback for learners. The AIS in this study incorporates a learner model using Decision Trees and an instructor model that integrates feedback from experienced seafarers with the goal of enhancing skill acquisition in a simulated environment. The study compares the performance of participants trained with AIS to those trained without it. Participants completed three training scenarios and one test scenario in a simulator, with key performance metrics used to assess training effectiveness, such as the changes in ice concentration for a specified zone. Statistical analyses, including normality assessments and independent samples t-tests at a significance level of p < 0.05, were conducted to assess performance differences. The findings demonstrate AIS's transformative potential to enhance ice management performance.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".