Abstract
Maritime domain is one of the most high-risk industry and it predominantly employs simulator training as a means to train the prospective operators. Maritime instructors need to train students who will become future sharp end operators and interact in highly safety critical environment. One of the goals of maritime education is therefore to create relevant learning activities among the trainees which will help them not only to learn the highly contextualized/situated knowledge of work settings but who are able to work together in team demonstrating qualities such as critical thinking and leadership. Educational frameworks that take into account the unique nature of maritime domain are therefore needed to be identified. Sociocultural perspectives suggests that learning is situated in communities of practice. In order to study how people learn in practice, it is necessary to study how individuals interact with other members and the different mediational tools that are at their disposal for engagement in specific activities. In this exploratory study, we analyze the maritime simulator training through the lens of sociocultural perspective and propose computer supported collaborative learning as an intervention for maritime education and training. We describe the salient characteristics of collaborative learning and its relevance for maritime education and computer supported training. An observation study was carried out at a maritime simulator training facility. The aim is to better understand/explore the complexities of maritime simulator training and reflect on how the collaborative learning approach can be better used to support the connections of simulated learning activities and its relevance in real life maritime operations.
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Sharma, A., Nazir, S., Wiig, A.C., Sellberg, C., Imset, M., Mallam, S. (2019). Computer Supported Collaborative Learning as an Intervention for Maritime Education and Training. In: Nazir, S., Teperi, AM., Polak-Sopińska, A. (eds) Advances in Human Factors in Training, Education, and Learning Sciences. AHFE 2018. Advances in Intelligent Systems and Computing, vol 785. Springer, Cham. https://doi.org/10.1007/978-3-319-93882-0_1
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