Last month I (Bob) visited a local elementary school for a “Science Alliance” visit. This is a program in our community to being local scientists into the classroom. I brought the Cartoon Network simulator I have been developing (Calin-Jageman, 2017, 2018). This simulator is simple-enough that kids can use, but complex enough to generate some really cool network behaviors (reflex chains, oscillations, etc.). The simulation can be hooked up to a cheap USB robot, so kids can design the ‘brains’ of the robot, giving it the characteristics they want (fearful–to run away from being touched; aggressive–to track light), etc.

The kids *loved* the activity–the basic ideas were easy to grasp and they were quickly exploring, trying things out, and sharing results with each other. They made their Finches chirp and dance, and in the process discovered recurrent loops and the importance of inhibition.

In developing Cartoon Network, my inspiration was logo, the programming language developed by Seymour Papert and colleagues at MIT. I was a “logo kid”–it was basically the only thing you *could* do on the computer lab my elementary school installed when I was in second grade. Logo was *fun*–you could draw things, make animations…it was a world I wanted to explore. But Logo didn’t make it terribly easy–as you went along you would need/want key programming concepts. I clearly remember sitting in the classroom writing a program to draw my name and being frustrated at having to re-write the commands to make a B at the end of my name when I had already typed them out for the B at the beginning of my name. The teacher came by and introduced me to functions, and I remember being so happy about the idea of a “to b” function. I immediately grasped that I could write functions for every letter once and then be able to have the turtle type anything I wanted in no time at all. Pretty soon I had a “logo typewriter” that I was soooo proud of. I could viscerally appreciate the time I had saved, as I could quickly make messages to print out that would have taken me the whole class-period to code ‘by letter’.

Years later I read Mindstorms, Papert’s explanation of the philosophy behind Logo. This remains, to my mind, one of the most important books on pedagogy, teaching, and technology. Papert applied Piaget’s model of children as scientists (he had trained with Piaget). He believed that if you can make a microworld that is fun to explore, children will naturally need, discover, and understand deep concepts embedded in that world. That’s what I was experiencing back in 2nd grade–I desperately needed functions, and so the idea of them stuck with me in a way that they never would in an artificial “hello world” type of programming exercise. Having grown up a “logo kid”, reading Mindstorms was especially exciting–I could recognize myself in the examples, and connect my childhood experiences to the deeper ideas about learning Papert used to structure my experience.

Papert warned that microworlds must be playful and open-ended. Most importantly a microworld should not be reduced to a ‘drill and skill’ environment where kids have to come up with *the* answer. Sadly, he saw computers and technologies being used that way–to program the kids rather than having the kids program the computers. Even more sad, almost all the “kids can code” initiatives out there have lost this open-ended sense of exploration–they are mostly a series of specific challenges, each with one right answer. They do not inspire much joy or excitement; their success is measured in the number of kids pushed through. (Yes, there are some exceptions, like minecraft coding, etc… but most of the kids code initiatives are just terrible, nothing like what Papert had in mind).

So, what does all this have to do with statistics? Well, the idea of a microworld still makes a lot of sense and is also applicable to statistics education. Geoff’s dance of the means has become rightly famous, I would suggest, because it is a microworld users can explore to sharpen their intuitions about sampling, p values,CIs, and the like. Richard Morey and colleagues recently ran a study where you could sample from a null distribution to help make a judgement about a possible effect. And, in general, the use of simulations is burgeoning in helping researchers explore and better understand analyses (Dorothy Bishop has some great blog posts about this). Thinking of these examples makes me wonder, though–can we do even better? Can we produce a fun and engaging microworld for the exploration of inferential statistics, one that would help scientists of all ages gain deep insight into the concepts at play? I have a couple of ideas… but nothing very firm yet, and even less time to start working on them.. But still, coming up with a logo of inference is definitely on my list of projects to take on.

I’m going to end with 3 examples of thank-you cards I received from the 3rd grade class I visited. All the cards were amazing–they genuinely made my week. I posted these to Twitter but thought I’d archive them here as well.

This kid has some great ideas for the future of AI

“I never knew neurons were a thing at all”–the joy of discovery
“Your job seems awesome and you are the best at it”—please put this kid on my next grant review panel.
  1. Calin-Jageman, R. (2017). Cartoon Network: A tool for open-ended exploration of neural circuits. Journal of Undergraduate Neuroscience Education : JUNE : A Publication of FUN, Faculty for Undergraduate Neuroscience, 16(1), A41–A45. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/29371840
  2. Calin-Jageman, R. (2018). Cartoon Network Update: New Features for Exploring of Neural Circuits. Journal of Undergraduate Neuroscience Education : JUNE : A Publication of FUN, Faculty for Undergraduate Neuroscience, 16(3), A195–A196. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/30254530

I'm a teacher, researcher, and gadfly of neuroscience. My research interests are in the neural basis of learning and memory, the history of neuroscience, computational neuroscience, bibliometrics, and the philosophy of science. I teach courses in neuroscience, statistics, research methods, learning and memory, and happiness. In my spare time I'm usually tinkering with computers, writing programs, or playing ice hockey.

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