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ViSTPro: Spatiotemporal processes visualization in engineering education and crisis training

Sifakis Ioannis, Arapi Polyxeni, Moumoutzis Nektarios, Christodoulakis Stavros

Πλήρης Εγγραφή


URI: http://purl.tuc.gr/dl/dias/5448711C-F949-43EA-AA64-C65C7B764D0B
Έτος 2017
Τύπος Πλήρης Δημοσίευση σε Συνέδριο
Άδεια Χρήσης
Λεπτομέρειες
Βιβλιογραφική Αναφορά Y. Sifakis, P. Arapi, N. Moumoutzis and S. Christodoulakis, "ViSTPro: spatiotemporal processes visualization in engineering education and crisis training," in IEEE Global Engineering Education Conference, 2017, pp. 413-422. doi: 10.1109/EDUCON.2017.7942880 https://doi.org/10.1109/EDUCON.2017.7942880
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Περίληψη

Spatiotemporal processes are a generic model for representing various types of content knowledge of physical processes like the ones studied in geosciences, life sciences and diverse specializations of engineering. There is a need to model such content knowledge in a systematic way and provide open exploration environments for learners to interact with the defined spatiotemporal models. ViSTPro provides a generic infrastructure that addresses these needs of science and engineering education, thus fostering active explorations of spatiotemporal processes as rich scenarios authored by educators and offered to learners through a web-based player. Playing a scenario involves graphic representation of formations, movements and interactions on Google maps. Learners interact with graphical entities in an intuitive manner. In contrast, traditional ways of learning depend upon a painful and difficult process to develop abstract mental images with no real-world direct mapping. Using ViSTPro, learners can watch the representation of the processes' evolution in space and time, and actively intervene. To demonstrate the flexibility of ViSTPro and its capability to provide insightful visualizations, a pilot application is elaborated focusing on emergency situations and events with the aim to support the training of civil protection professionals and volunteers. This application enables innovative training methods, through the use of Google maps and complex scenarios pertaining to spatiotemporal processes that model the phenomena under consideration as well as linking to important information that is represented through ViSTPro semantic maps.

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