FrameIT: Detangling Knowledge Management from Game Design in Serious Games

Kohlhase M, Bösl B, Marcus R, Müller D, Rochau D, Roux N, Schihada J, Stamminger M (2020)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2020

Publisher: Springer

Pages Range: 173 - 189

Conference Proceedings Title: Lecture Notes in Computer Science, vol 12236

Event location: Bertinoro

ISBN: 978-3-030-53517-9

DOI: 10.1007/978-3-030-53518-6_11

Open Access Link: https://kwarc.info/people/mkohlhase/papers/cicm20-frameit.pdf

Abstract

Serious games are an attempt to leverage the inherent motivation in game-like scenarios for an educational application and to transpose the learning goals into real-world applications. Unfortunately, serious games are also very costly to develop and deploy. For very abstract domains like mathematics, already the representation of the knowledge involved becomes a problem.

We propose the FrameIT Method that uses OMDoc/Mmt theory graphs to represent and track the underlying knowledge in serious games. In this paper we report on an implementation and experiment that tests the method. We obtain a simple serious game by representing a “word problem” in OMDoc/Mmt and connecting the Mmt API with a state-of-the-art game engine.

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How to cite

APA:

Kohlhase, M., Bösl, B., Marcus, R., Müller, D., Rochau, D., Roux, N.,... Stamminger, M. (2020). FrameIT: Detangling Knowledge Management from Game Design in Serious Games. In Springer (Eds.), Lecture Notes in Computer Science, vol 12236 (pp. 173 - 189). Bertinoro, IT: Springer.

MLA:

Kohlhase, Michael, et al. "FrameIT: Detangling Knowledge Management from Game Design in Serious Games." Proceedings of the The 13th Conference on Intelligent Computer Mathematics (CICM 2020), Bertinoro Ed. Springer, Springer, 2020. 173 - 189.

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