ABSTRACT
In the domain of Marine Education and Training (MET), simulators have been utilized for the purpose of training seafarers in the norms for avoiding collisions or for developing the skill of ship manoeuvrability, and even the operation of machinery in the engine room, as well as for conducting research on the subject matter of ship structure, specialized vessel operation, working principle of equipment, and shipboard safety training. These tools are even more important when facing disruptive events such as the COVID-19 pandemic. In MET institutions, full-mission bridge and engine room simulators have been utilized for teaching seafarers for more than a decade. A Systematic Literature Review (SLR) was conducted to identify immersive and non-immersive simulator applications produced over the previous ten years to improve seafarers' experiential teaching and learning, in the maritime domain. We retrieved 27 articles using the four stages of PRISMA paradigm: Identification, Screening, Eligibility, and Inclusion. The selected papers were read and analyzed according to the training type, the area of training, and the technologies used. The utilization of immersive and non-immersive simulators in the context of the MET domain has been identified and mapped. A few research studies (9 out of 27) compared immersive and non-immersive simulator-based training with conventional training. The quality and efficacy of immersive and non-immersive simulator training at MET institutions have been studied. A model from the learner's perspective is essential and recommended for future research to assess efficiency and efficacy.
ABSTRACT
In situations like the coronavirus pandemic, colleges and universities are forced to limit their offline and regular academic activities. Extended postponement of high-stakes exams due to health risk hereby reduces productivity and progress in later years. Several countries decided to organize the exams online. Since many other countries with large education boards had an inadequate infrastructure and insufficient resources during the emergency, education policy experts considered a solution to simultaneously protect public health and fully resume high-stakes exams -by canceling offline exam and introducing a uniform assessment process to be followed across the states and education boards. This research proposes a novel system using an AI model to accomplish the complex task of evaluating all students across education boards with maximum level of fairness and analyzes the ability to fairly appraise exam grades in the context of high-stakes examinations during SARS-CoV-2 emergency. Basically, a logistic regression classifier on top of a deep neural network is used to output predictions that are as fair as possible for all learners. The predictions of the proposed grade-awarding system are explained by the SHAP (SHapley Additive exPlanations) framework. SHAP allowed to identify the features of the students' portfolios that contributed most to the predicted grades. In the setting of an empirical analysis in one of the largest education systems in the Global South, 81.85% of learners were assigned fair scores while 3.12% of the scores were significantly smaller than the actual grades, which would have had a detrimental effect if it had been applied for real. Furthermore, SHAP allows policy-makers to debug the predictive model by identifying and measuring the importance of the factors involved in the model's final decision and removing those features that should not play a role in the model's “reasoning” process.