Waiting for Data

Waiting for “Superman” follows 4 children in the broken public school systems of three states in the US. It chronicles the struggles they face with their families in their respective attempts to get better educations and thus access to more opportunities for success later on life. It’s a very well-written and directed film and, despite being made by a decidedly privileged individual, manages to capture the desperation created by such a failed system experienced by the families it follows. I’ll try not to give too many spoilers throughout this post so that you can fully enjoy it when you watch it (which will be soon, because you should watch it)

I didn’t initially think to link Superman to my work this summer, but as I watched it I began to see more and more connections and realized that I could learn a lot from this documentary. Two key things stood out:

1. Poverty and good education are not mutually exclusive

This is the point driven home by Geoffrey Canada, CEO of Harlem Children’s Zone. The film points out that it was once thought that so-called “bad neighbourhoods”–those stricken with poverty and the crime that inevitably follows–create bad schools, but that the new data suggests the opposite is true: bad schools create “bad neighbourhoods” (sidenote: that term is very problematic, but it’s what the film used and conveys a generally-understood concept). To me, this connection seems obvious, because basic education is where the foundations of many other things are built. If you don’t have good education, then you can’t build the foundations of, for example, a healthcare system. Education is a key point in influencing the poverty cycle, and Superman does a good job of bringing the details of that influence to the forefront.

What is good education, though? That’s hard to say, and the film never really arrives at a satisfying answer. Sometimes it seems to say that meeting the accepted grade level of skill is good, while at other points, it explicitly says that covering more material is better. Mr. Canada repeatedly highlights that good teachers are the key to a good education. So, what makes a good teacher? Having had amazing educators throughout my formative educational years (damn, I’m lucky to have only had one bad teacher–Mrs. Hammell in grade eleven math), I have a feeling of what a good educator looks like. However, translating that feeling to measurable metrics is a whole other story and one which I’m sure I’ll have to take a crack at writing over the course of the summer.

In any case, if a good teacher is the key to good education, then poverty ought not to have too heavy an effect on good education. One might conjecture that poverty would reduce the pool of people available as good educators for many reasons. However, it can also be assumed that good teachers still exist in an impoverished area (the film shows examples of this) and are therefore key to improving education in that area. Additionally, if this is true, then building schools in African countries is not so useful, because the physical location where education takes place is not the major factor influencing the effectiveness of the education. Obviously these assumptions are based on the Western case studies provided in the film, so I’m taking them with a grain of salt and viewing them as thinking points, rather than fact.

2. Data is important in determining the sources of problems and potential solutions

Data is used extensively throughout Superman and most of it has been hotly contested*. Regardless of the controversy around the data used in the film, isn’t it amazing that the data were complete, analyzed, and accessible? We take for granted how rapidly we can access large amounts of data for decision-making. Literally every day before I get dressed, I check the weather online and decide what I’ll wear based on the forecast. This is a small example of the power of data and the film does a very good job of showing just how useful data can be by using data collected by the US Government to very succinctly describe the issues and complexity of the broken American education system.  While the data do not point to one solution, they do give a starting point to the problem.

This brings up a key aspect to the work EWB does in general; it’s one thing to generate a sustainable data system and a whole other for that system to be effective. The data must influence key decision-makers, who must understand what it means. That understanding requires analysis skills, which require education. Whoa: education, again. That’s why EWB provides training to, well, everyone who will benefit. And it’s why part of my job will involve providing training for effective use of data (in a nutshell). Beyond that, it’s why education has a sort of two-pronged relationship with poverty. Good education can not only help people achieve success in socioeconomic systems that currently exist, but it can also lead to the improvement of those situations by individuals educated well in those systems.

In Engineering, this is called positive feedback, and is generally something to be regulated, because it leads to out-of-control systems and rapid depletion of resources. In this case, though, the resources–bright Ghanaian minds–are unlimited, and the system has not benefited from control in the past.

So that’s what I want, folks: a system that’s simply out of control.

-C

*-I think the author of the article I linked missed some key points and came at his analysis from a heavily-biased perspective himself. Watch the film and judge the biases of all involved for yourself.

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One response to “Waiting for Data

  1. I love how you NAME your one bad teacher. BRB LINKING THIS TO HER 😛 😛 (lol jk)

    Excellent thoughts & analysis – I’ll definitely have to check that out! I’ve seen Geoffrey Canada’s interview on TCR, and there were a few quality New Yorker articles on the subject, but the documentary sounds very interesting.

    The point about data is interesting as well, because one of my issues with all the new methods of obtaining large amounts of data (ex. crowdsourcing) is that they don’t always explore new ways of using it and its practical application, but I really like the weather example. Data existing isn’t enough: it has to be applied, used, and understood (or understood as closely as data can be).

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