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Embedding teaching-student-research nexus in 2018: a case study in big data analysis in nautical services
Vervoort, M.; Van Casteren, R.; Decorte, C. (2018). Embedding teaching-student-research nexus in 2018: a case study in big data analysis in nautical services, in: Gómez Chova, L. et al. EDULEARN18. 10th International Conference on Education and New Learning Technologies, Palma, Spain. 2-4 July, 2018. pp. 4204-4209
In: Gómez Chova, L. et al. (Ed.) (2018). EDULEARN18. 10th International Conference on Education and New Learning Technologies, Palma, Spain. 2-4 July, 2018. IATED Academy: [s.l.]. ISBN 978-84-09-02709-5. xc, 11293 pp. https://dx.doi.org/10.21125/edulearn.2018
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Documenttype: Congresbijdrage
Author keywords
    teaching-students-education-research nexus, big data analytics, nautical services, discrete stochastic simulation, estimated time of arrival (eta)
Auteurs  Top 
  • Vervoort, M.
  • Van Casteren, R.
  • Decorte, C.
Abstract
    Becoming aware of the variety of ways that teachers and students experience and apply research in higher education sets all key holders to become more reflective of the environment in which learning is taking place. With the increasing need to implement teaching-student-research nexus, self-responsibility and self-motivation is moving to the forefront of higher education. The paper is based on the findings of a research project that is implemented into bachelor and master final works at the Antwerp Maritime Academy. The project involves the following field 'Big Data analytics in Nautical Sciences' with implementation of its work towards Estimated Time of Arrival (ETA) of vessels at the Port of Antwerp and with collaboration of the Antwerp Port Authorities. We examine how research knowledge can generate innovative outcomes that meaningfully benefit a wider set of key-holders. A first research goal of the case study is to discover systematics in ship delays in the Port of Antwerp with Big Data Analytics methods. This value was established by subtracting the Actual Time of Arrival (ATA) from the ETA in Automatic Identification System (AIS) when passing Flushing. Big Data opens up new perspectives in data analysis. Algorithms derived from more and more data can be improved real-time. This becomes a requirement since shipping companies are reinventing themselves towards service providers rather than transporting the goods alone. They do so by controlling and optimizing the whole supply chain (e.g. Damco, part of the Maersk group). The most important factor here is time, since often take the biggest part of the supply chain, the need for accurate estimated times of arrival is the greatest with the ships. Further research shall focus on refining the model and making the model self-learning, where little or no human interaction is needed anymore. The model will be compared against the existing model of the port of Antwerp. The optimization of nautical services is the next step in our combined research as the nautical services have great resource costs. The Marine Pilotage service can then be analyzed and as such determine the required number of marine pilots for vessel traffic flow towards the Port of Antwerp. This will be evaluated by a stochastic, dynamic and discrete simulation for safe service standards in pilotage. Areas like optimization of the number of pilots, tugs, resource allocation problems and many more complex and inherently stochastic problems can be reliably solved by simulation.
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