Monday, February 29, 2016

Van Lehn paper

Agent based modeling through starlogonova seems to be a good way of modeling the Zika virus for students. Not only does it give students a reasonably intuitive opportunity to learn the basics of computer science, it also provides enough power for students to create simplistic models of disease transmission. The open ended nature of CS and the project-sharing capabilities of slnova also provide an environment for students to tinker and explore beyond the bounds of the lesson (e.g. "I wonder what happens if I do this?" or "How can I account for this other variable we didn't mention") both on their own time but also in collaboration with other students. While there is a reasonably steep learning curve, I suspect that students exposed to CS earlier in life may have a surprisingly easy time picking it up.

Another good option is the constraint model, which could theoretically be implemented into the agent based modeling system. Basically the constraint model takes a lot of inputs of different variables, then spits out what results could or could not happen. This could be an important way of testing how certain risk factors will affect the spread of disease. For example, students might be able to explore the effectiveness of bug nets or fogging on disease transmission, or look into how a particularly rainy rainy season might affect levels of standing water which might affect mosquito population. Creating these models would force students to think critically about the complex relationships between all the players in a given system and demonstrate how tiny changes in one area can have huge changes downstream in ways that wouldn't seem obvious at first.

I think one thing that could be a hurdle is not teaching students enough about Zika or about the mosquito vectors before setting them loose on model creation. A key part of this process that I think is somewhat glossed over is the research and legwork needed to gather the tools and build the foundation of successful models. This research could be tied in with the modeling process "What do we need to know about x to implement it into our model?" but I feel like it could be very easy to get caught up in the apparent success of modeling that we could gloss over some of the more foundational aspects of learning.

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