How Do We Avoid Dabbling (So We Can Actually Learn)?
dabble (verb): to take part in an activity in a casual or superficial way
Let’s say you’ve decided to begin tackling a big, vague, multidisciplinary question in the year 2020.
And let’s say, hypothetically, that question is whether or not you want to go to graduate school and why.
In this hypothetical scenario, you may begin by creating a list of questions you would need to answer in order to answer the ultimate big, vague, multidisciplinary graduate school question. Then, you may realize you need to prioritize the pile of questions somehow.
You determine that answering why should come before answering how. So you begin taking courses, reading books, listening to podcasts, watching videos, researching potential future job descriptions, and following people you could learn from on social media… in every subject you deem relevant to answering that why graduate school question.
At the same time.
Suddenly, your hypothetical brain hurts, and you feel like you aren’t getting anywhere.
There has to be a better way… right?
In 2020, I’ve listened to Scott Young’s book Ultralearning on Audible twice already.
Needless to say, I’m a fan.
Scott Young defines ultralearning as deep self-education to learn hard things in less time. The technique focuses on learning depth-first, breaking impasses down into prerequisites you can finish step-by-step, creatively using resources and balancing theory with practice. He also clearly states that ultralearning works best when applied to specific, measurable, well-defined goals.
Because when your learning projects get too wide and multidisciplinary, you run the risk of dabbling in a lot of subjects but never really learning anything.
Sound familiar?
The problem is that this particular graduate school learning project I’m working on is multidisciplinary. The question of whether someone should go to graduate school or not is big and vague. The question of what problem someone should work on for the rest of their life (and whether or not graduate school would allow them to do that) is even bigger and more vague.
How do we avoid dabbling when taking on a big, vague, multidisciplinary learning project?
To be honest, I haven’t quite figured this out yet. But I’m working on a strategy informed by ultralearning and my own initial approach to this project (which, frankly, didn’t work). Here is what I’ve learned so far.
Rethink depth
In a subject like calculus, you might be able to get through a test or two by memorizing formulas. But at some point, you will hit a wall if you don’t know why you are doing things. Memorizing is a short-term bandaid. If you are interested in applying your knowledge to a variety of different scenarios and building on your initial knowledge to tackle more advanced problems, you need to establish a strong foundational understanding (also known as depth).
Since I’m from a STEM background, this is how I wanted to approach my graduate school problem. I wanted to establish a basic skill set in each subject I thought was relevant, then apply the combined knowledge to the practical why and how questions I need to answer.
Sounds simple, right?
Unfortunately, I quickly realized that subjects like philosophy don’t have “basic skill sets” the same way that calculus does. Ultimately, it may not be possible to establish a basic skill set to solve practical problems after a quick deep dive into some subjects.
I’m also not entirely sure which mix of subjects I should be learning. At this point, there may be relevant things I should learn that I’m simply not aware of yet. Put simply, I don’t know what I don’t know. This uncertainty about what to study led me to effectively try to learn everything at once (and, unsurprisingly, not learn much at all).
So I have two problems on my hands: how to establish a practically-applicable understanding of subjects that are inherently more interdisciplinary, vague, or theoretical and how to do so without devoting too much time to each subject since I am still trying to establish a general understanding of multiple fields (and may need to pivot to learning other things as I learn more about what I need to learn).
How could we solve these problems with an ultralearning approach?
At this point, I’m thinking of “establishing depth” as “establishing a framework”
Outside of STEM subjects (and to some extent within STEM subjects), understanding enough jargon to know whether or not what you are learning is relevant to the question you are asking can be a challenge. While it may not be possible to establish “basic skill sets” for solving philosophical problems after a quick deep dive, it may be possible to build a framework to help focus your learning (so you at least know whether or not what you are reading is applicable to the problem you are trying to solve).
Primary framework goal: avoid getting buried under a pile of jargon every time you learn a new subject.
We can still use some of the tenants of ultralearning to establish a framework, like creatively using resources for deep self study. For this purpose, I’m using introductory online courses and books that provide a general overview of the subject (including definitions of the most widely-used jargon). If the subject has something like schools of thought or basic tenants, those are exactly what I’m trying to learn in an initial deep dive. Knowing those organizational frameworks makes it much easier to know where things I learn in the future “fit” within the overall discipline, which helps with retention. Knowing the general layout of the field also helps me determine where I might need to drill down to establish more depth through additional study.
One framework at a time
When I decided on this approach, I made a mistake with the implementation. I tried to do this general introduction/survey of several different fields at the same time. The reason I did this had some logical basis: I’m not planning to apply what I learn in a vacuum (one subject at a time), so I’m not convinced I should be learning in a vacuum either.
While I’m still drawn to the idea of integrating my learning, this approach didn’t work very well at the survey/intro level. Without some initial focused attention on each individual subject, it’s difficult to establish a foundational framework. Without that foundation, I was just dabbling (and not retaining much of what I was learning).
Going forward, I’m planning to continue with the idea of doing short deep dives, but I’ll only do these deep dives into one subject at a time. Initially, I’m planning to spend no longer than a month building an introductory framework in each subject. I’m currently in the middle of both a statistics for data science course and a philosophy course that I intend to finish this month. After these are completed, I’ll choose one of those two topics (or one of the other topics on my list) to focus on individually and make sure I’ve established enough of a framework to make some determination about what I should study next before I move on to the next topic.
My dabbling days are (hopefully) behind me.
Have you applied ultralearning to a big, vague, multidisciplinary learning project? How did you address the problem of needing to cover a variety of topics, but also establish depth? Thanks for any ideas or insights.
