Atomizing Knowledge

Paper gold stars, pinned next to your name, signaling your achievement at a task, may have constituted your first encounter in kindergarten with a leaderboard. It kept track of your successes, directed you towards the next task, and perhaps gave you bragging rights within your local community. That gold star may or may not mean much to the rival kindergarten down the street.

 

The present day high-tech equivalent? Digital badges. Micro-credentials. And if you string enough of these gold stars together, you might be awarded a nano-degree. At a scale of 10^-9, you’d need a British billion of these to reach a degree. We’re reaching the atomic scale of knowledge, broken down into its bits and bites. And to store it somewhere, we’ll need bytes.

 

While the culture of assessment is certainly a contributor to this trend, and the reductionism of ‘being scientific’ plays its part, my post today is to muse about how educational technology has influenced the atomization of knowledge. Some of this is discussed more eruditely in Neil Selwyn’s Education and Technology that I’ve been reading. I highly recommend his balanced and thoughtful book to the reader interested in pursuing these topics. But on to my musing.

 

Atomizing knowledge in ‘factory’-like schools was seen as a good thing at the tail-end of the industrial revolution in the early twentieth century. Instead of patchwork and varied curricula of one-room schoolhouses, the ‘modern’ school was considered standardized and efficient, just like its factory counterpart. In the present-day, the factory-like aspects are a routine punching bag for pundits of education, technology, and of course, politicians. Technology is heralded as savior, breaking the strictures of school, and having the power to unleash your creativity through freedom of exploration. Personalized education is the new watchword – tailored, crafted – God forbid it be industrially produced.

 

Teachers are often blamed for their ‘resistance’ to this evolution. Or revolution. They should move away from being ‘sage on the stage’ to ‘guide by the side’ or perhaps even ‘peer at the rear’. No, I didn’t come up with that last one on my own. Selwyn mentions it in his book, which suggests it has some widespread use. I’d never heard it before and hope it doesn’t perpetuate. I realize my blogging about it seems antithetical to my hope. Such is the power of sticky, funny-sounding, buzz-phrases.

 

But teachers might have good reason to be suspicious of the technology that claims to assist them but is not-so-secretly attempting to replace them, despite protestations of the entrepreneurs seeking the killer app holy grail of education. Some of us educators are being recruited in that effort. Sometimes there is the offer to elevate myself from an expert to a rock-star expert. Other times, it seeks my ‘valuable’ contribution to the Robot, the Singularity of A.I. For educational purposes, of course. I received yet another e-mail this morning about such an opportunity. In the past, I used to send them directly to spam without looking. But nowadays I take a brief glance to see what the public face of the edtech startup is purporting to deliver to its clients. Then I send it to spam.

 

How do you train a robot to do a human’s job with precision and efficiency? By atomizing the tasks. Since I’m in the education business, it’s the atomization of knowledge. That’s the basis of how educational adaptive systems work, at least in the present paradigm. (I’m not smart enough to predict the future paradigm.) There is learning and cognitive science to back-up some of the A.I. approaches. I personally find Cognitive Load Theory to be a useful framework when designing my course and its activities. There are many helpful practices we’ve learned to help make things stick for students. Duolingo even sent me a message earlier this year explaining its ‘techniques’, for example why it occasionally uses funny and amusing phrases.

 

When you’re teaching something that’s new and challenging – chemistry for example! – it really helps to break things down into bite-sized pieces. Some of it needs to be pre-digested. Others need to have the texture for students to chew on for a while. I am in the business of atomizing knowledge, although in my case the pun is also clearly intended because conceptual chemistry aims at the scale of atoms and molecules. This reductionist analytical approach is useful, but I pair it with building-up synthetic approaches that are not so easy to describe. I’m not just hedging. There’s a good reason for this. I think learning is a complex process, not just a complicated one, which means it cannot be reduced to the sum of its parts. The parts are the things most easily measured. It’s the ‘science’, if you will.

 

How does the Robot adaptively figure out when you’ve learned something and awards you the digital badge? By asking questions. And if you answer them ‘correctly’ then it assumes you have learned. Those correct answers are based on the programmers’ putting together the ideas of subject-matter experts, then run through test-users (students), and the data is analyzed. Rinse. Repeat. This is how the machine learns. A beta-release has now codified some of the ‘best practices’, subject to tweaking with more machine-learning data. Sounds like a factory operation to me. Tailored and crafted to what end? A standardized credential that can be exchanged for other factory goods. Gold star trading.

 

I won’t pretend to know what my students have learned with the precision of a machine. What I can offer them is an ongoing conversation. I do ask them questions and I try to elicit responses. Some knowledge will likely be passed. Hopefully their thinking will be challenged and expanded. And ideally, good decisions will be made in their lives based on wisdom derived from knowledge and understanding. None of this will pass muster in the assessment reports. For that, they’re looking for the atomization of skills. Not even knowledge.