TITLE: Computational Thinking Everywhere: Experiments in Education AUTHOR: Eugene Wallingford DATE: October 17, 2011 4:46 PM DESC: ----- BODY: I recently ran across Why Education Startups Do Not Succeed, based on the author's experience working as an entrepreneur in the education sector. He admits upfront that he isn't offering objective data to support his conclusions, so we should take them with a grain of salt. Still, I found his ideas interesting. Here is the take-home point in sentences:
Most entrepreneurs in education build the wrong type of business, because entrepreneurs think of education as a quality problem. The average person thinks of it as a cost problem.
That disconnect creates a disconnect between the expectations of sellers and buyers, which ends up hurting, even killing, most education start-ups. The old AI guy in me latched on to this paragraph:
Interestingly, in the US, the people who are most willing to try new things are the poor and uneducated because they have a similar incentive structure to a person in rural India. Their default state is "screwed." If a poor person doesn't do something dramatic, they are going to stay screwed. Many parents and teachers in these communities understand this. So the communities are often willing to try new, experimental things -- online education, charter schools, longer school days, no summer vacation, co-op programs -- even if they may not work. Why? Because their students default state is "screwed", and they need something dramatically better. Doing something significantly higher quality is the only way to overcome the inertia of already being screwed. The affordable, but poor quality approaches just aren't good enough. These communities are on the hunt for dramatically better approaches and willing to try new things.
Local and global maxima in hill-climbing

I've seen other discussions of the economic behavior of people in the lowest socioeconomic categories that fit this model. Among them were the consumption of lottery tickets in lieu of saving, and more generally the trade-off between savings and consumption. If a small improvement won't help a people much, then it seems they are more likely willing to gamble on big improvements or to simply enjoy short-term rewards of spending. This mindset immediately brought to mind the AI search technique known as
hill climbing. When you know you are on a local maximum that is significantly lower than the global maximum, you are willing to take big steps in search of a better hill to climb, even if that weakens your position in the short-term. Baby steps won't get you there. This is a small example of unexpected computational thinking in the real world. Psychologically, it seems, that we are often hill climbers. -----