That’s why I’m so excited by the prospect of eliminating educational friction, solving the thermodynamic efficiency of education, and building machines that make people insanely skilled as efficiently as possible.
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I’m so jealous of how good the next generation of students will have it. And that’s the goal. That’s how it should be.
After decades of the edtech industry being soft and unserious, it’s inspiring to see hardcore folks heading in that direction – people who take optimizing learning in students’ brains as seriously as the quantitative finance industry takes optimizing return in the stock market.
I’ve wanted alien-level edtech ever since I was a kid. And not just in hindsight as an adult – literally, in my early teens it’s something I actively thought about and wished I had. That’s why I’m so motivated to help build it nowadays.
For me it started the year I took precalculus at school. I encountered a bit of calculus in the spring, and it seemed really cool, being the highest level of math you hear of as a typical kid and the way that movies often communicate that a character is a genius. So I figured I’d try teaching myself the rest over the summer using various online resources.
Self-study turned out to be way more efficient than I was used to at school, and it was incredibly fun making progress so quickly. Once I got to optimization, related rates, and basic differential equations, I was having so much fun wielding calculus like a weapon and opening cans of whoop-ass on modeling problems, that I voluntarily holed up in my room working out math problems. It was almost like playing a video game, except, the longer I played, the more proud my parents were of me for working so hard.
After calculus, I immediately moved on to Linear Algebra and Multivariable Calculus through MIT OpenCourseWare, and once school started up again in the fall, I just kept on going with the rest of undergraduate math (plus half of physics and a bit of mathy coding).
I was completely obsessed, to the point of self-studying about 8h/day over that summer, and then maybe 6h/day during the school year. (I self-studied on the sly during school – I typically I had to hide what I was doing, act like I was paying attention, and keep an ear out in case I got called on.)
But at the same time, I was also frustrated by all sorts of inefficiencies I encountered during the learning process. And although I got pretty far, if I were to have spent the equivalent amount of time on the most effective adaptive learning platforms of today, that would have been life-changing for me – I mean, life-changing compared to my intense MIT OCW / textbook self-study, which was already life-changing compared to traditional school.
Just to name a handful of inefficiencies that I encountered:
- Not super scaffolded $\to$ you periodically run into situations where you bang your head on a wall thinking “how the heck did they get from here to there?” and it takes a long time to figure out what kind of logical leap is happening (if you figure it out at all)
- Doesn’t track your knowledge / make sure you’ve mastered the prerequisites for anything new you’re supposed to learn $\to$ you often feel a large gap between your level of knowledge and the new material, which leads to more banging your head on a wall trying to figure out what prerequisite knowledge you’re missing and how to learn it
- No spaced review $\to$ you quickly get rusty on a lot of what you learn, which not only means you come out of the course having forgotten a lot of content, but even during the course, you’re constantly forgetting prerequisites
- Doesn’t adapt to your level of performance $\to$ you waste a lot of your time doing the wrong amount of work. Sometimes you grasp a topic quickly and end up doing way more practice problems than you need; other times you struggle with a topic and don’t do enough practice problems to reach mastery
- Leaves the definition of “mastery” open to interpretation by the learner $\to$ as a learner, it’s hard to know when you’ve mastered something well enough to continue moving forward. You often think you’ve learned something well enough, when you actually haven’t — but you won’t know unless there’s an expert who is evaluating your knowledge. On the flipside, you can also take things too far being a perfectionist, spinning your wheels on the same topic for a week over some minor point that doesn’t make perfect intuitive sense to you, when it would be more productive to just keep moving forward and solidify your understanding by building on top of it.
- Not enough successful problem-solving experiences $\to$ in a typical college course, you might solve 50-100 homework problems all semester. There’s so much educational friction that it takes you 20+ minutes to struggle through each problem, often not even getting it right until you give up and look at a solution or get help at a TA session. But if the problems were broken down and presented in a more finely scaffolded sequence, and each problem only presented once you’ve mastered the prerequisites, then you could get through problems much faster, say, just a couple minutes per problem on average. That’s what a good tutor would do: scaffold your learning experience so that you’re solving a problem every couple minutes. They would build up your learning in bite-size increments. You could get through 10x as many problems that way, with a much higher success rate.
- Not enough knowledge audits $\to$ most college courses have only a handful of exams throughout the entire semester (and even grade school classes seldom have more than one quiz per several weeks). But quick, frequent timed quizzes — say, 15 minutes every couple days — are a powerful way to engage in retrieval practice and build automaticity while simultaneously identifying weaker areas in need of additional practice.
- Not enough targeted remediation $\to$ it’s rare to find a resource that gives you additional practice after you miss a question on an assessment. At best, you might take it upon yourself to review the questions you missed. But it would be far better to complete a battery of additional problems like each one you missed, until you’re able to successfully and consistently solve those problems — and then you’d want to evidence that knowledge on a quiz retake (with different problems of the same types).
… I could keep going with this list, but you probably get the point: all of these things introduce unproductive friction into the learning process, leading you to make less educational progress per unit time/effort that you put towards learning.
That’s why I’m so excited by the prospect of eliminating educational friction, solving the thermodynamic efficiency of education, and building machines that make people insanely skilled as efficiently as possible.
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