What is the most effective way to learn a skill or topic? Scott Young believes that the way to answer that question is to design a learning project, experiment with multiple techniques, and report on the results. For the past thirteen years, he has been doing that on his blog and in his online classes. Later this year, his book Ultralearning will be released, with advice for those of us who want to succeed at similar projects.
In 2016, Scott wrote a series of articles and a manifesto on Ultralearning. Then he got to work on the book. The result is a guidebook with the same philosophy as his earlier writing, but with details filled in, a wide range of techniques explained, references to learning research, and stories of people who have successfully used an ultralearning approach.
Here’s the ultralearning philosophy in a nutshell: You’re responsible for your learning outcomes. If the study process you’re using isn’t working, find a new one using research and experimentation. If you’re taking a class and the assigned textbook is incomprehensible, get a better one.
Besides this philosophy, the theme of intensity runs through the book. Just as you could describe an ultramarathon as a more intense version of a marathon, ultralearning is a more intense version of learning. Although none of the principles of ultralearning explicitly advise you to work harder, Scott makes a point of calling out the intense effort of the people he profiles. For example, there’s the Scrabble champion who bicycles for hours while rehearsing words, the Toastmasters competitor who visits multiple clubs per week to practice, and the future Jeopardy! winner who spends months drilling on every question and answer in the game’s history.
Those examples convey a difference in emphasis between Scott’s advice and that of James Clear, who wrote the foreword to Ultralearning. In his own recent book, Clear advises habit aficionados to make their habits easy (the Third Law of Behavior Change) to help solidify new behaviors. But despite the apparent contrast, I think the two systems work well together, with the Atomic Habits system providing a way to ease into the behavior changes required to execute a successful (and intense) ultralearning project.
Ultralearning begins with a review of Scott’s previous learning projects, an introduction to a few of the ultralearners we will meet in the book, and an argument for the necessity of ultralearning in the modern world. It ends with an approach to designing an ultralearning project, some alternatives to ultralearning, and a final chapter-length ultralearning case study. The middle of the book consists of nine chapters covering the nine principles of ultralearning.
The principles of ultralearning are a set of methods that Scott has observed in other ultralearners and has found to be effective in his own projects. Together with the ultralearning philosophy, they define what ultralearning is. The more a learning project adheres to the philosophy and principles, the argument goes, the more it’s likely to get results like the projects described in the book.
If the term ultralearning sounds like a buzzword, here’s another way to look at it: Scott has studied people who have accomplished interesting and impressive feats of learning. And he has conducted his own learning experiments. The philosophy and principles are his effort to distill the approach that makes these projects successful.
Here are the nine ultralearning principles.
A perennial entry on lists of the most popular online courses is Coursera’s Learning How to Learn. “Learning how to learn” is one way to describe the concept of metalearning. Because ultralearning means being responsible for your own learning outcomes, it also means taking responsibility for your learning plan. If you spend time before a project thinking about your learning goals, what you want to learn, and how you plan to learn it, you’ll get more out of the project. Then once the project starts, you can keep metalearning in mind and make adjustments to the plan as you go.
The principle of focus isn’t specific to learning. It’s a skill required for many types of mental and physical achievements. But focus (and the lack thereof) has become an issue in recent years in the context of online distractions, as described in Cal Newport’s recently published Digital Minimalism, and its predecessor, Deep Work.
In Ultralearning, Scott has a few things to say about procrastination, distraction, and how long you should stick to one task. He also describes some interesting results on finding the appropriate level of focus for a task, not just striving for maximum focus: According to the Yerkes–Dodson law, there’s an optimal level of “arousal” for different tasks. A task like making a basketball free throw requires high arousal, while solving a math problem requires lower arousal, or may benefit from a complete lack of arousal as you allow your subconscious to work on the problem.
This principle relates to the problem of transfer, the process of taking learning acquired in one context, like a classroom, and applying it in another context, like the workplace. This turns out to be very difficult to do, and is perhaps the biggest weakness of the traditional education process.
The directness principle says you should design your ultralearning project to match the context where you plan to apply what you learn. In the book, there’s an example of an architecture student applying for jobs. Like most students, his portfolio includes typical student projects. But his job search gets better results when he designs a building of the type his target employer specializes in and produces it using the design software they use.
The trade-off with directness is that it can lead to a lack of focus on fundamentals. For example, a programming boot camp teaches specific skills used at programming jobs, but neglects the theoretical background that formally-trained software engineers use to understand why hardware and software work the way they do. So the directness principle shouldn’t be used as an argument that all education must be immediately practical.
There’s a tendency in learning projects to practice the skills you’re best at. Since you can do them well, it feels like you’re getting a lot done. With skills you’re not good at, you spend more time being stuck. But the way to get better is to drill on your weakest skills, since those are the ones holding you back.
In a simplistic view of education, you study to learn a topic, then take a test to see what you learned. But evidence from learning research indicates that testing, even if you’re a novice in a topic, is far more effective for learning than reading a textbook or listening to lectures. This is the principle of retrieval: spend as much time as possible recalling what you know about a topic, even to the point of testing yourself on it before you start studying, to prepare your brain for the information to come.
Element #3 of Geoff Colvin’s deliberate practice framework says that in a deliberate practice program, “Feedback on results is continuously available.” You may be drilling your weakest skills and using retrieval techniques rather than passive review. But unless you have a way to evaluate the results of these efforts, you might just be repeating the same mistakes.
The most effective feedback comes from an expert coach who designs a training plan specifically for you and tells you what you’re doing right and wrong as you carry out the plan. But other kinds of feedback are also useful, including something as simple as selecting practice problems for which you have an answer key.
Although we have advanced technologies for looking up information, they don’t completely replace human memory. As fast as it is to ask Google or Alexa to retrieve a fact, it’s even faster to remember something you have studied effectively. And to use a search engine or digital assistant, you still have to formulate a good query, which requires some amount of domain knowledge. Finally, this all assumes that you have access to these tools, which you may not if you’re taking in an exam, taking part in an in-person contest, or traveling out of cellular range.
Ultralearning covers the theory of remembering and forgetting, and suggests four memory mechanisms: spacing out your learning, turning skills into procedures, learning beyond basic competence, and using mnemonics.
It would be nice if after you learned a topic, you had not just a collection of facts and procedures, but a flexible understanding that allowed you to answer deep questions about it and seamlessly connect it to other topics. Scott argues that such an intuitive understanding is a result of learning things from the ground up. If your practice consists of solving math problems using procedures, then your understanding will be procedural. But if you work on hard proofs, you’ll be forced to understand topics at a deeper level.
To verify that you really understand something, and to develop your intuition, Scott has long recommended the Feynman Technique, which tests if you can explain a concept from scratch.
The ultralearning principles start with metalearning and end with another meta principle, experimentation. In theory, you could throw out the other principles and just experiment to find what works for you. Scott Young uses an experimental approach in his own work, designing learning projects and trying out techniques. But there’s no need to invent every learning process from scratch. And if you want the processes to work for people other than yourself, you have to check sources other than just your own experience. So Ultralearning includes research citations and reports of how other learners have used these principles. Those stories provide a jumping-off point for your own experimentation.
In the book, Scott makes the point that experimentation becomes increasingly important the more advanced you are in a skill. If you’re learning a moderately popular skill, you’ll find plenty of introductory tutorials. But as you develop expertise, you join a shrinking group of people who have dedicated time to studying that skill, so there are fewer resources available. Furthermore, experts are opinionated about how they work, so a ready-made process wouldn’t be appropriate. But if you want your learning to be as effective as possible, you can’t just use whatever processes you happen to pick up. You have to experiment.
Ultralearning is filled with far more examples and ideas than I mentioned in this article, and I look forward to trying them out in my own projects. Next week, I’ll cover some ideas about applying ultralearning to the study of math.
(Image credit: Revise_D)
I based this article on a review copy of Ultralearning, which was provided by the author.