Engaging with (complex) real-world systems requires that engineers are able to model and computationally simulate their behavior. While this practice is of key importance to modern engineering practice, engineering students rarely engage with the model-building process - an indispensable tool when classic models reach their useful limits, and thus a crucial component of the scientific process. Specifically, students are rarely asked to construct novel models for physical systems. Instead, they are typically asked to re-derive and mathematically analyze existing models. Consequently, students also rarely practice verification and validation of models.
This study presents a framework for introducing, motivating, and engaging entry-level undergraduate engineering students in the computational modeling process through a participatory scenario of social infectivity. The proposed full-class activity, dubbed the “meme game,” features an exchange of doodles which results in the viral propagation of certain doodles across the player population. The activity makes explicit connections to epidemiology and sociology via design choices which offer direct analogies between the game and real-world scenarios of infectious disease spread and social mimetics/information spread. In the wake of the COVID-19 pandemic and the proliferation of disinformation on social media, these domains associated with our activity have become especially relevant to real-world practice.
We used the “meme game” as a first-day activity for a course on modeling and simulation at a small engineering-focused college. Our results suggest that the proposed activity successfully provides immediate exposure to an interesting physical system to which an array of accessible reduced-order modeling approaches can be applied, each with advantages and limitations. Since the activity and analysis relies on relatively little background knowledge, first-year STEM students can effectively engage with the concepts presented in the game. Additionally, the activity results in a rich student-generated data set which motivates a variety of questions about viral phenomena, and offers the opportunity for students to meaningfully answer these questions by validating their models against real-world measurements.
This paper will describe the full logistics of the activity, including methods for collecting and synthesizing the generated data, and qualitative analysis of outcomes from our implementation of the meme game. A framework for incorporating the activity into broader lessons on the importance and practice of scientific modeling is discussed alongside implications on approaches to undergraduate technical education.
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