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Ecosystem Simulator

A Learning Game About Genetic Algorithms

  • Conference paper
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Serious Games (JCSG 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13476))

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Abstract

Genetic algorithms (GA) are a subclass of machine learning methods that allow an automated determination of optimal points. Learning games seem to be very well suited for teaching GA principles, although learning games are still not state of the art. Accordingly, this article presents the development and evaluation of the learning game Ecosystem Simulator for teaching GA principles. The development cycle is described here, which includes the selection of a game theme, the identification of the learning content, the definition of the game mechanics to two subsequent iterations consisting each of development and evaluation. The evaluation of the second development iteration’s prototype reveals attaining high scores both for learning motivation and intrinsic motivation along with a significant increase in knowledge. Thus, a learning game has been developed, which, in view of its rather young development timeline, seems to offer an appealing gaming experience combined with decent learning outcomes. All in all–as a motivating learning game–the Ecosystem Simulator enriches GA teaching.

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Acknowledgements

The authors gratefully acknowledge the financial support provided by the German Federal Ministry of Education and Research (BMBF) through grant FKZ 033W011B provided for the “TWIST++” project. Any opinions, findings, conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the institution mentioned above.

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Authors and Affiliations

  1. Bauhaus-Universität Weimar, Marienstraße 15, 99423, Weimar, Germany

    Qianqi Huang, Heinrich Söbke & Tom Lahmer

Authors
  1. Qianqi Huang
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  2. Heinrich Söbke
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  3. Tom Lahmer
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Correspondence to Heinrich Söbke .

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Editors and Affiliations

  1. Bauhaus University, Weimar, Weimar, Thüringen, Germany

    Heinrich Söbke

  2. Technical University of Berlin, Berlin, Germany

    Pia Spangenberger

  3. TU Darmstadt, Darmstadt, Germany

    Philipp Müller

  4. TU Darmstadt, Darmstadt, Germany

    Stefan Göbel

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Huang, Q., Söbke, H., Lahmer, T. (2022). Ecosystem Simulator. In: Söbke, H., Spangenberger, P., Müller, P., Göbel, S. (eds) Serious Games. JCSG 2022. Lecture Notes in Computer Science, vol 13476. Springer, Cham. https://doi.org/10.1007/978-3-031-15325-9_14

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  • DOI: https://doi.org/10.1007/978-3-031-15325-9_14

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  • Online ISBN: 978-3-031-15325-9

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