Emergence and the Future of Schools: Preparing for Data-Driven Systems

EdTech, Learning

Kristin Garn

I’m convinced that the contemporary measure of a topic’s popularity is confirmed when it debuts on Netflix. Last week, to my pleasant surprise, that network’s algorithms suggested that I watch the series: “How We Got to Now With Steven Johnson”. Considering that Johnson is the author of one of my favorite books on emergence theory and its applications, Emergence: The Connected Lives of Ants, Brains, Cities, and Software, I’d say, score one for robust recommendation engines (and, possibly, my over-sharing of data).

Johnson has been a popular champion of using emergence theory to help understand and anticipate the complexities of our increasingly connected world. In my own corner of complexity (educational data), he has helped me to consider how to prepare for a digitally connected future in which increasing educational data will also mean more opportunities for interpretation and intervention. He has also alerted me to the reality that this will bring about a new system which will be irreversible in its complexity. In other words: it’s not going to get any simpler.

In physics, simple systems always decay toward more complex ones, and never vice versa. Over time, or even spontaneously, organized systems disintegrate into chaos. Big Bangs become expanding universes in much the same way as the spoken word turns from dialects to languages and into written, then digitized, and then globalized communication.

Consider a piece of glass as a simple, unified system of tightly connected pieces. Now, balance that glass at the edge of a high table. Give it a nudge until it falls to the floor and breaks into hundreds of pieces. You could wait a thousand years and it is highly unlikely that those shards of glass will independently organize themselves back into a piece of glass. The complex doesn’t easily become simple.

If we can imagine a future education system which has increasingly expanded into branched networks of categories and subcategories of globally connected data points, then we can think of a structure that could attempt to digitize the very foundations of learning such as meaning, teaching and knowledge.

Add to that entanglement all the other multiple student measurements that could connect to public data collection. This possible future contains a world where a student could be measured in terms of geolocation and biometrics attached to external social data such as weather, traffic and markets, not to mention behavioral input.

So, if our educational future follows the laws of physics and our education systems increase in complexity it might be important to consider things like: regardless of measurement, what are the limits to our interpretation?

Rather than seeking answers, it could be more important to pursue the right questions during early emergent times, such as:

  • How can we develop educational planning which is consistent with the new science of complexity?
  • Can we make an analysis of “meaning-making” during these times of rapid organizational change?
  • Regardless of our capabilities of measurement, what are the limits to our interpretations of data?

Johnson suggests that our era of emergence has entered a new phase in the past few years which could prove to be more revolutionary than before. His models include the recommendation systems that actually pointed me right back to him. Like the Netflix algorithms, the most adaptive complex systems are those which will become smarter over time. However, it will always remain up to us to decide what truly constitutes “smarter.”

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Kristin Garn is the CEO of Mathtoons Media. Follow Kristin on Twitter, @mypracti.


benjamin /

“Simple” is not necessarily the opposite of “complex”. Weather systems are simple and complex, for instance. We have learned a lot about weather systems over the years and can explain the (simple) processes that lead to thunderstorms, tornados, etc.; but due to their complexities, we cannot predicted with 100% certainty that these thunderstorms, tornados, etc., will occur. Education is similar. We have learned a lot about the science of teaching and learning which constitute many very simple processes (I.e., fractals), but due to its complexity, there is also an art to education. Like the weather, we cannot predict with a 100% certainty that a student will learn. But we can still “predict” in education (like the weather) when learners are more likely to learn when certain (simple) processes are in place. Indeed, teaching and learning remain both an art and a science.