Brady Fukumoto (@br80io) header
Brady Fukumoto (@br80io) profile picture

IBJJF Masters World Champion. Building @fluencyflower

San Diego, CA
Joined March 2014
Most folks who think they are bad at math are really just bad at arithmetic. "I used to be good at math," they muse. "I got As in elementary but wasn't smart enough for fractions (or long division or algebra)" It's not your fault, it's the flawed system. Here's proof. (1/5)
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Rounding from 9.8 to 10 is great for anyone wanting to learn more about the world from a physics perspective without getting lost in the weeds. After all, 9.8 is a simplification too. Actual gravity averages closer to 9.81 and varies with elevation and latitude.
I was helping my daughter with her physics homework last year. One of the problems had g=10 m/s2 and I said "it's 9.8 m/s2" to which my daughter replied "my teacher told us to use 10 m/s2 because it's easier." I couldn't believe it! This was unthinkable when I went to school.
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Brady Fukumoto retweeted
It starts as early as arithmetic. Students who are slow on their basic math facts begin falling behind their faster peers as soon as multi-digit arithmetic. That's what researchers found in the study "Lack of Automaticity in the Basic Addition Facts as a Characteristic of Arithmetic Learning Problems and Instructional Needs." "The slowest cluster reported use of counting strategies on many bundles; the fastest cluster reported use of retrieval or efficient-thinking strategies. Cluster group was the best predictor of performance on multidigit tasks. ... Analysis by error type showed most errors on the multidigit sums were due to fact inaccuracy, not algorithmic errors. The implication is that the cognitive demands caused by inefficient solutions of basic facts made the multidigit sums inaccessible."
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Brady Fukumoto retweeted
Many people knock edtech's obsession with motivation. Allow me to defend it quickly: AI is going to become better than humans at doing nearly every subject. Why would a student choose to learn at all? The answer is awe, excitement, surprise, love. Learning is worth it because of how it changes our relationship to the world. School isn't about teaching you how to solve a^2 + b^2 = c^2, but that "omg did you know that there's literally an equation embedded in the universe that explains why when you cross now instead of waiting to get to the crosswalk, you get there more quickly than your friend does"!!! When everything can be done for us, we have the privilege of becoming more human in our pursuit of that thing.
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Brady Fukumoto retweeted
When should students memorize math facts? I wouldn't ask a student to memorize times tables until: 1. They understand that multiplication is repeated addition. 2. They’re able to calculate times tables using that definition. But students don’t always have to derive things before memorizing them. For instance, in university math it’s efficient to: 1. Memorize derivative rules first. 2. Later derive them from scratch. In general, a student should loosely understand what something means before memorizing it. This does not always have to be a derivation / full computation. It’s just that in arithmetic, full computation turns out to be the most straightforward way to understand things. In calculus, if you want to loosely explain what the derivative means, then derivations are not as conceptually straightforward. Instead, it’s more straightforward to just explain that the derivative represents the instantaneous rate of change, or the slope of the tangent line to the curve. With that information, it makes sense that, e.g.: - The derivative of x^2 should get larger and larger as x increases. - The derivative of sin(x) oscillates up and down. That’s the main thing I’m trying to get at: The reason why it’s helpful to “loosely understand what something means” before memorizing it is that it makes the memorization process a lot easier. And the goal should always be to approach that baseline understanding in the most quick / easy / straightforward / simple way, whatever it may be.
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Brady Fukumoto retweeted
Learn linear algebra. Learn to visualize matrices as spatial transformations. Learn to see calculus as the language of optimization. Learn vector spaces until neural networks stop looking like black magic and start looking like high-dimensional geometry. A deep understanding of math transforms you from an API-wrapper developer into an AI architect.
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Brady Fukumoto retweeted
Learn math to think with brutal clarity. Learn math to spot bad arguments instantly. Learn math to model complex systems in your head. Learn math until the chaotic noise of modern life collapses into clean patterns. You don't need a university degree. You just need to stop viewing math as equations and start viewing it as stories.
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Math is really the only field where answers can be absolutely true, 100% of the time without exception.
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Brady Fukumoto retweeted
For the vast majority of kids, memorizing basic math facts is not a big ask. Math education is a weird world where people fight wars against the mathematical equivalent of learning to tie one's shoes. Like, it's obviously super useful and it's not that freaking hard! Just do it!
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I think it's possible to learn to use AI without knowing how to code by starting small and working up to bigger projects, asking good questions along the way. Asking good questions is a much more important skill than knowing all the answers in the age of AI.
Replying to @theo
How can newbies learn how to use AI if they don't write code first? Nobody seems to talk about this. The only reason anyone understands any software architecture at all is because they went through the process of learning and writing code. We only seem to think about the developers of today using AI.
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Brady Fukumoto retweeted
Math ed constructivist on X: "No one is against standard algorithms" Reporter in recent news clip below: "Is carrying the one [standard algorithms] a thing of the past?" Head of @NCTM w/ big smile: "I want to say yes" youtu.be/EzKzhg1ee7o?t=148
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Brady Fukumoto retweeted
You're going to see a lot of pushback like this from math teachers on the right side of this problem. And I appreciate @mpershan's gentle pushback on the narrative that all math teachers resist explicit instruction and memorization of math facts, as well as @Stephen_Sawchuk's work on surveys of the field. But we still have a multitude of issues. A quarter to a third of math teachers with doubts about the importance of fact fluency is still a lot of math teachers! And just like with phonics: if the field doesn't agree on the volume of practice needed to achieve automaticity, and if almost every math curriculum lacks ample practice items to ensure kids get to fact fluency, and the field doesn't use good assessments to ensure kids are achieving automaticity with math facts, and if the US has practically zero state screening initiatives in math, to reinforce the importance of fact fluency, you have a pretty big systemic issue. Teachers can think they're teaching math facts explicitly and effectively, and you can still have tons of kids slipping through the cracks. teapawt.net/mpershan/status/210583…
A nice thing that Ed Week did (I think @Stephen_Sawchuk was deeply involved) was survey math education professors and teachers on teaching questions. Math edu professors were split 50/50 on the importance of memorizing math facts. But teachers were way more positive on it.
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Once fluent, you can learn to express unique ideas in new ways. Both these methods lead to the exact same result 100% of the time. Both trivially easy to perform for anyone fluent in arithmetic. The logic is elegant, poetic. Discovery is the reward for fluency.
116/4 is 100/4 + 16/4, so =25+4, thus 29. A good alternative to long division, as NCTM might’ve recommended in 1989
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On one hand, everyone seems to agree that memorizing multiplication tables is good, a worthy goal in pursuit of fluency. On the other hand, no one actually seems to agree on anything at all.
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Deliberate practice can be quite enjoyable if you have the foundational skills to engage in the interesting bits, and if the practice design is well-crafted and constrained around those interesting bits.
The most hard-hitting 2 sentences in all of talent development research:
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Every athlete feels nervous before big competitions. If you aren't, you probably don't care about the outcome. Learning to manage nerves and perform under pressure is a big benefit of competitive sports. Math isn't different. Math anxiety is real and worth learning to overcome.
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"We don't rise to the level of our expectations, we fall to the level of our training."
"no clear relationship between test anxiety and examination performance is found ... There is also no suggestion of meaningful differences across key sub-groups"
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If you think athletics coaches have anything close to a consensus about the most effective training methodologies, especially hours of repeated drilling, you haven't been around athletics recently.
People will happily commit their children to professional sports and coaches who require hours of repeated drills and practice. But when it comes to math, suddenly we hear that “too much practice” is a bad thing. If athletes want to develop automaticity, they practice the skill repeatedly. If we want a student to develop automaticity in math, we ALSO need them to practice the skill repeatedly. Practice isn’t the enemy to be avoided. Practice is how skills become automatic.
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Brady Fukumoto retweeted
The way you make hard things easy is by mastering subskills. When you're automatic on basic arithmetic, solving equations feels like swiftly moving puzzle pieces around, and you just need to identify how they fit together. But without automaticity on basic arithmetic, each puzzle piece is a heavy weight. You struggle to move them at all, much less figure out where they're supposed to go. It's the same with all skill trees.
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Brady Fukumoto retweeted
Right answers do matter in mathematics. And furthermore, answers that are not just right, but also reasonably fast. Why? Because you gotta develop automaticity on low-level skills in order to free up mental resources for higher-level thinking! If you don't, then low-level actions will constantly be interrupting your flow of thought and you won't be able to see the forest for the trees. Think about all the skills that a basketball player has to execute in parallel: they have to run around, dribble the basketball, and think about strategic plays, all at the same time. If they had to consciously think about the mechanics of running and dribbling, they would not be able to do both at the same time, and they would not have enough brainspace to think about strategy. The same thing happens in math. For instance, solving equations feels smooth when basic arithmetic is automatic -- it's like moving puzzle pieces around, and you just need to identify how they fit together. But without automaticity on basic arithmetic, each puzzle piece is a heavy weight. You struggle to move them at all, much less figure out where they're supposed to go. It’s the same all the way up the ladder of math: from arithmetic, to algebra, to calculus, to all sorts of university-level math and beyond. If you try to advance to the next rung on the ladder without standing stable on the previous rung, then you’re going to fall off the ladder. And even if you get saved by grade inflation and manage to make it up a step without completely falling off, it’s the next step that will get you.
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