A Farewell to Alms https://www.amazon.com/dp/0691141282/
Cartesian Economics https://www.amazon.com/dp/1616407395/
The 10,000 Year Explosion https://www.amazon.com/dp/0465020429/
The Righteous Mind https://www.amazon.com/dp/0307455777/
– Philosophy –
Tao Te Ching https://www.amazon.com/dp/0060812451/
– Autobiography –
Surely You're Joking, Mr Feynman https://www.amazon.com/dp/0393316041/
Recollections of Eugene Wigner https://www.amazon.com/dp/0738208868/
– Fiction –
Fahrenheit 451 https://www.amazon.com/dp/030747531X/
If you are interested in learning more about this mindset, you should read Mindstorms by Seymour Papert (RIP).
Scratch can be a "gateway drug" to languages that professional programmers use. The extensions/abstractions of Scratch from Berkeley that deal with making it do complicated things seem like putting a fish on a bicycle. Sometimes, you just have to leap and try to not fall.
I have a thesis that the kind of thinking required to survive med school is diametrically opposed to the kind of thinking required to do statistics well. It's the "rote pattern matching" versus "mathetic language fluency" issue that's at the heart of things like Papert's Constructivist learning theory and it really causes me to have little surprise at an article like this. Doctors are (usually) viciously smart people who have to make a wide array of difficult decisions daily, but to operate at that level requires an intuition around a lot of cached knowledge, something I feel to be basically the opposite of statistical thought.
I don't think this is unique, either. It's the heart of Fisher's program to provide statistical tests as tools to decision-makers. It's an undoubted success in providing general defense against coincidences to a wide audience, but it casts the deductive process needed in a pale light.
I think a principle component of the computer revolution is to provide more people with better insight into mathetic thought. Papert focuses on combinatorial examples in children in Mindstorms but I think the next level is understand information theory, distributions, and correlation on an intuitive level. MCMC sampling went an incredible way to helping me to understand these ideas and probabilistic programming languages are a great step toward making these ideas more available to the common public, but we also need great visualization (something far removed from today's often lazy "data viz").
Ideally, things like means and variances will be concepts that are stronger than just parameters of the normal distribution---which I feel is about as far as a good student in a typical college curriculum statistics class in a science or engineering major can go---but instead be tightly connected to using distributions accurately when thinking of complex systems of many interacting parts and using concentration inequalities to guide intuition.
I think the biggest driver of the recent popularization of Bayesian statistics is that distributions as a mode of thought is something quite natural to the human brain, but also something rather unrefined. People can roughly understand uncertainty about an outcome, but have a harder time with conjunctions or risk. How can we build tools that will teach people greater refinement of these intuitions?
"By deliberately learning to imitate mechanical thinking, the learner becomes able to articulate what mechanical thinking is and what it is not. The exercise can lead to greater confidence about the ability to choose a cognitive style that suits the problem. Analysis of "mechanical thinking" and how it is different from other kinds and practice with problem analysis can result in a new degree of intellectual sophistication. By providing a very concrete down-to-earth model of a particular style of thinking, work with the computer can make it easier to understand that there is such a thing as a "style of thinking". And giving children the opportunity to choose one style or another provides an opportunity to develop the skill necessary to choose between styles. Thus instead of inducing mechanical thinking, contact with computers could turn out to be the best conceivable antidote to it. And for me what is the most important in this is that through these experiences these children would be serving their apprenticeships as epistemologists, that is to say learning to think articulately about thinking."
That's the work you need to read - but really, learn about the man, learn about Logo. As others have noted, it's more than just turtle graphics - so much more. Unfortunately, educators still have not grasped his ideas fully, and if you look closely, what is often touted out there for teaching children and others programming - is essentially his ideas, reimplemented poorly.
He has written more on the subject than that one book; and his thoughts and ideas (and Logo itself) aren't really about teaching children programming, but teaching children how to think computationally, algorithmically. He saw how and where things were heading long before many others, and he worked to try to get people prepared. Sadly, all people grasped was turtle graphics, but not the larger picture.
I often wonder where we'd be today had more people truly understood and implemented his (and, to be honest, his "muse" / "mentor" / "inspiration", if you will, in Piaget) methods and thoughts on teaching. Most likely in a much better position as a society...