“When everything is connected to everything else, for better or for worse, everything matters.”
I heard this in a recent TED talk from Manuel Lima, expert in visual data analysis and researcher of human thought-mapping. While Bruce Mao, designer and founder of the Massive Change Network, was the originator of this piece of wisdom, Lima used it in his talk to discuss not only the historical mapping of human cognition, but also the importance of making connections and forming networks.
For whatever reason, my first inclination when trying to analyze a problem is to break the it into smaller parts, or to put each of the pieces in little boxes. By doing so, I’m able to find root causes or discrete components that might give me insights about the function or form of the whole. And this aligns relatively well with Lima’s visual history of human cognition, in the sense that our ancestors started here, too. They made trees that represented knowledge and broke it up into little pieces, whether it was Darwin using a tree to represent many of the world’s species or governments trying to display and classify the laws and philosophies of their nations.
And I, too, have been making trees for quite some time, but I make them for something different. I make trees for standards instead.
Up until this point, making trees for standards has made perfect sense. Since we’ve managed to group standards by subject (i.e., ELA, Math, Science, etc.), it has made grouping and visualizing them hierarchically rather easy. Math, Reading, Writing, and Language can be broken down into domains, each of which can then be broken into clusters composed of granular standards, telling specifically what students should know and be able to do at various grade levels. To a certain capacity, this hierarchical organization has oriented me in the standards, and in some cases, has even helped me see a level of connection between standards. In fact, I still see its purpose in this capacity. Moreover, it’s helped me see the form and function of mathematics and language in the context of an elementary classroom. Admittedly, though, I’ve always felt like something was missing from the standards.
After all, we know that learning doesn’t happen in a hierarchy. Instead, it happens three-dimensionally: it’s an amalgamation of linear and non-linear processes, a serendipitous firing of neurons, the product of contextualized interaction. As a result, I’ve begun to imagine learning–and the bits and pieces that make up a learning experience—as points in a circle, sphere or cylinder. Much to my surprise, Manuel Lima, in his talk on the visual history of human cognition, is showing me that I’m not the only one trying to visualize knowledge in this way. Interestingly enough, our profession is one of mapping knowledge and understanding. It’s one of finding these discrete pieces, meanwhile teaching holistically, and fostering our students’ abilities to make connections between them. The most accurate depiction of this mapping is one of a three-dimensional web, a series of dots and lines interwoven throughout space, spontaneously but consistently becoming thicker and more complex with time, very similar to neurons firing across three-dimensional spaces in the brain.
In fact, there are striking similarities between the ways in which knowledge and experiences are visualized in different disciplines. Many of these circular diagrams, which Lima calls “radial convergence” diagrams, look almost identical when decontextualized. His most striking display, which comes at the end of his talk, shows two images juxtaposed—one of the inner workings of a mouse’s brain, the other of the Millenium Simulation, the “largest and most realistic simulation of cosmic structure” to date. Both of these images reveal the power of a network on both small and large scales.
Paradoxically, our standards sets, while written relatively well, in my opinion, lack the structure that reflects this network of knowledge and experience. As a result, this translates into our data capture for student learning, as well. Many schools’ visualizations of student data require simply filling in boxes with colors, not necessarily making connections and developing a network of standards. They don’t see the process of student learning as connecting two seemingly unrelated concepts, building bridges between disciplines, or creating an interwoven fabric on top of which a child can stand; instead, they view it as filling in boxes and checking off standards. But learning doesn’t happen this way. Neurons in our brains don’t fill up containers; neurons in our brains fire across space and strengthen the connections through repetitive firings.
It’s not entirely difficult to see why we have not yet begun to represent student learning and data capture in this manner, though . This sophisticated technology not only requires relatively enhanced coding skills, but it also has simply not reached the education space quite yet. In short, we simply don’t yet have the data capabilities to map, track, and measure data in this format.
But imagine a world where we do.
Imagine a time when we do not only measure learning by the dots a student has collected, but instead can measure learning by the dots a child has connected, the strength of those connections, and the innovative creations that come as a result of those connections. Imagine a time when we can measure that a child has not only been exposed to a certain standard, piece of academic content, or social-emotional experience, but that a child has made a connection between two or more of these standards, content areas, or experiences, that we could, perhaps, then measure the strength of those connections through the amount of exposure and the competency the child demonstrates.
By defining this new visualization of standards-based data and student learning through radially convergence, while seemingly esoteric at first glance, it could bolster our ability to create interdisciplinary experiences, meanwhile understand the content through which our children make the most connections. It could pave the path to interest-based experiences, connection-based learning, and developing students on passion rather than simple compliance to competency-based mandates.
Most of all, by reimagining our learning taxonomies, we can show that we, as a profession, are learning from our predecessors, that we no longer need to put everything into little boxes, but that instead, we can measure what’s important. And what’s important in the classroom is the network of relationships we build — relationships between others, relationships between the environment, and relationships between the knowledge, experiences, and novelties we encounter every day.
That’s what I want to capture. That’s what I want to measure.