2019, Pustulka, Elzbieta, Hanne, Thomas, Richard, Wetzel, Egemen, Kaba, Benjamin, Adriaensen, Stefan, Eggenschwiler, Adriaensen, Benjamin

We want to lower the entry barrier to optimization courses. To that aim, we deployed a game prototype and tested it with students who had no previous optimization experience. We found out that the prototype led to an increased student motivation, an intuitive understanding of the principles of optimization, and a strong interaction in a team. We will build on this experience to develop further games for classroom use.

2019, Pustulka, Elzbieta, Hanne, Thomas, Adriaensen, Benjamin, Eggenschwiler, Stefan, Kaba, Egemen, Wetzel, Richard, Blashki, Katherine, Xiao, Yingcai

We aim to improve the teaching of the principles of optimization, including computational intelligence (CI), to a mixed audience of business and computer science students. Our students do not always have sufficient programming or mathematics experience and may be put off by the expected difficulty of the course. In this context we are testing the potential of games in teaching. We deployed a game prototype (design probe) and found out that the prototype led to increased student motivation, intuitive understanding of the principles of optimization, and strong interaction in a team. Ultimately, with the future work we sketch out, this novel approach could improve the learning and understanding of optimization algorithms and CI in general, contributing to the future of Explainable AI (XAI).