Abstract
An international research team from Canada, United States, and Hong Kong developed a novel solution for creating interoperable, scalable learning objects along a gradient from single interactive objects for one learning activity to articulations of thousands of learning objects that become simulations capable of automatically assessing complex conceptual and performance competencies. We call this solution Research Oriented Adaptive Decision Modeling Architecture Platforms for Simulations – ROAD-MAP. Our acronym, ROAD-MAP, acknowledges the many pathways that can lead to developing educational simulations and serious games. Such pathways are not well-mapped at this time, especially in the context of how such simulations and games actually improve higher-order reasoning and pattern recognition. ROAD-MAP provides a generalized solution for building simulations and serious games within an evidence-based approach to design, development, and evaluation of new types of coupled research and teaching-learning-assessment environments for different discipline domains.
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Tashiro, J., Hung, P.C.K., Martin, M.V. (2010). ROAD-MAP for Educational Simulations and Serious Games. In: Tsang, P., Cheung, S.K.S., Lee, V.S.K., Huang, R. (eds) Hybrid Learning. ICHL 2010. Lecture Notes in Computer Science, vol 6248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14657-2_18
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