Everyone misses this problem solving step
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- čas přidán 13. 07. 2024
- There's lots of good advice on problem solving. But the details matter.
00:00 Introduction
1:08 The problem solving process
1:54 The important sentence
3:02 Skipping the step vs doing the thing
BTW, if you're learning physics on your own, I think Susan Rigetti's recommendations are indispensable. www.susanrigetti.com/physics
The "2000" problems video I reference is here: • What happens when you ...
References:
The physics book in question is: Young, H. D., Freedman, R. A., & Ford, A. L. (20). Sears and Zemansky's University Physics with Modern Physics. 15th Edition. Pearson education. Although I think any of the earlier editions (14th, 13th, international, etc.) are good. Again, credit to Susan Rigetti for this recommendation.
The papers I referenced on conceptual knowledge and procedural skill:
Kim, E., & Pak, S. J. (2002). Students do not overcome conceptual difficulties after solving 1000 traditional problems. American Journal of Physics, 70(7), 759-765. (available at sdsu-physics.org/sdsu_per/arti...)
Byun, T., & Lee, G. (2014). Why students still can't solve physics problems after solving over 2000 problems. American Journal of Physics, 82(9), 906-913. (available at aapt.scitation.org/doi/abs/10...)
This video feels like it's so important
And yet says nothing at all of importance?
This video feels like it was created to game the algorithm, keeping the actual important part of what he hints at here in another video.
I'd rather have a 2 hour long video where he covers the problem and solution in depth instead of some vague, "feel the answer" before you do the math advice that asks you to watch another video to get the answer, which will likely be a video where he hints at something and then advises you to watch a different video of his that also feels important but is ultimately devoid of substance and directs you to another of his videos to find the information.
This guy is likely good at getting people to engage with multiple videos of his, thereby gaming the algorithm.
Toodles. Enjoy the sense of importance and gravitas without anything to actually take away from this video.
@@J0vile I meant it genuinely. It felt like a really important concept to talk about.
You are like the absolute gold standard channel on learning and its sad you're criminally underated!! Keep making these pls!!! (Anyone reading like the video)
I lole how the painting behind is posing like "Oh. Damn! He is making a lot of sense"
Oh, hey, I do this! This is a generally applicable approach to building intuition/understanding. All you have to do is *always* make a prediction before you gather concrete information. For a simple example from my life, I always guess what time it is before I look at a clock, and because of that my internal clock is pretty damn sharp.
It sounds like this procedure is widely applicable even to abstract problems such as marketing or art. Trying to predict before executing, e.g. running an A/B test for marketing or painting for art. It sounds like you'd be creating a habit of gauging first whether something is good abstractly - using those abstract ideas to guide your decision making throughout execution phase.
I suppose for marketing you'd focus on ideas like
"how much of and which of these demographics are affected and how so?",
"what might this person say about this brand for this particular webpage?", or
"would this audience stay longer or shorter on the site when they first see this?"
and art would be about visualising it, asking questions about different parts like
"would this make the face look happier?",
"would this make the painting more vibrant?", or
"would this contrast poorly with that?"
I wouldn't know much about some kind of "learning loop" you'd employ. Mr. Keep, I wonder about your thoughts on working through abstract problems that don't have concrete answers as do the perfect worlds of maths and physics. What do you think about the application of mental models in step 2.5 of prediction? Things like inversion?
"What could I do to make the potential customer not want to buy at all?"
"What simple small thing could I do to ruin this painting?"
Or consideration of the extremes?
"If X goes to say a billion what does this look like?"
And so on...?
This is one step to understanding that anything that can be improved needs to have a feedback mechanism present.
It's basically going back and forth between future state/result and past. Then making changes to the next attempt as long as the change is satisfying.
The point I'm making is that, the application of this process is applicable to anything which has feedback mechanism present. Meaning, you can look at the future state and the past state and can compare them.
Description is very good
This is awesome. Very applicable also for coding.
In code reviews in my school I involuntary throw out ideas of what code does, when I read it and it sometimes annoys the evaluated, because they want to explain their code.
I always ask them, to give me some time to engage with their code, before they explain it.
And I make rather good progress.
I remember a similar diagram from my Chemical Principles book, from Atkins and Jones. Changes my thought process forever.
This is the genuinely most fascinating channel I have ever come across! Thank you for sharing your gift.
If anybody hasn't signed up for the newsletter, it is similarly a goldmine!
This is gold!
Thank you!
I think this also ties in with another video of yours about routine expertise and adaptive expertise,
I would assume this is probably one of the key steps in achieving adaptive expertise?
I have a question though if I learnt some theory (f.e. a mathematical concept & formula), exactly how many problems am I meant to solve,
or if posed in other words, how do I know if I solved enough problems? Because when I search online about adaptive expertise, I think I found some information about routine expertise being a building block of adaptive expertise, but do I need to be able to use techniques off the top of my head to achieve adaptive expertise?
My perspective has been: "understand the technique, and if you come across a problem that ends up needing the technique, figure out the steps manually (because I understood the why behind the steps) or just look it up". I only solve one problem or so to practice, but I am no longer sure if this perspective is enough to achieve adaptive expertise.
Would be interested what Ben replies with, but I know for myself I teach my students to fully model all relevant variables in the problem every time. No matter if the question asks for them or not. That way you rly start seeing how they are all connected, how they are all related to the scenario, and it makes every problem start to look the same.
I've had students tell me after we cover a topic that they are getting bored bc every kinematics or dynamics problem looks the same now, which is a good thing! They start classifying problems based on conceptual framework/familiarity instead of memorizing bespoke 'solutions' for specific variables one at a time that come with asterisks anyhow. If differently framed problems start looking the same in the sense they are easy, that's probably a good place to be such that you can seek out more novel problems.
I would also be interested in what he answers. One thing I know for certain is that I'm definitely not a fan of the very common idea to solve as many problems as you can. I feel like most advice around this question (e.g. on reddit) is to just keep solving problems and turning yourself into a pattern recognition machine.
While math and all related subjects that rely heavily on procedural skills DO need practice to get good at, I much prefer the idea from Justin Sung, that if you focus on the highest orders of learning (in most cases that's conceptual understanding), the details will often follow on their own. In this case I like to think of procedural skills (partly) as those "details", and I do in fact find that when I understand a concept well, I'll usually be able to solve it or basically be brushing against the solution but maybe missing some final touches on how to actually get to the solution, at which point there are very few gaps to fill and I don't think I need to solve many problems. When you understand things better, they stick with you much better and longer as well.
I think you're right that there's a connection. I find adaptive and routine expertise to be useful concepts, but they are not necessarily precise ones. As far as I know, the traditional way of thinking about adaptive expertise was "routine + more". At extreme levels, however, you might consider them to be conflicting forms of expertise. The more narrow and automatic your skill becomes, the less flexible it may be. Adaptive expertise is, in a way, about doubt and self-reflection - about NOT doing the automatic thing (at least at key moments). The routine/adaptive distinction doesn't really "cover" the expertise space - it's just one way of trying to understand what's going on.
How do you know you've solved enough problems? I'm not sure that you can know that. In an ideal world, you would intelligently sample from the entire space of possible problems, solving the problems that give you maximum insight into the variables at play in the domain. When problems start to get repetitive, that might be a sign that you've covered the space adequately. But it also might be a sign that you're not confronting the "interesting" problems (those that would cause you to think differently about the domain). As a simple heuristic, solving a large number of problems isn't bad - hopefully you'll have a good sense of the space at some point. But you also want to pay attention to the quality of the problems (i.e., how different they are from each other - is this new problem teaching you something you didn't already know or helping you to gain a skill you didn't already have?). And you also want to get as much value from solving the problem as you can (by considering similar problems, for instance, or generalizing the problem or extending it).
Very rare quality content video
Great video! Appreciate your work 🙌🏼
Thank you sir, your videos are definitely helpful.
This is a great use of logic for problem solving. But this does not limit using this concept to hard science. I teach people how to drive a truck, to get a Commercial Driver License. The toughest thing to learn is how to back a semi-truck properly. I tell my students the first thing to do is to imagine where the back wheels will be when they are done making an adjustment in their backing problem. Then how to get them there. For me, that is the point of Dr Keep's video - imagine the solution before solving the problem.
(I was completely distracted by the Go (wei-chi) board on the stand!)
That's an interesting example - thanks!
thank you so much,
the hard solving problem where the prediction depending on time it's really hard to find which steps should to combine to get solution
Commenting for the algorithm, keep up the good work!
This is a very important video
So accrute, thank you
This is applicable to learning creative writing.
Even as a student in ICS (justin's program, I recommend you check it out, his practical implementations are something I'd love for you to critique) I find incredible the work you're putting in here. I can help the students I tutor way more because of you, so for that I thank you.
I will be sharing this with my A-level Physics students (In China) next semester
Many thanks!
This is so important
I always ask my physics students to imagine the scenario they are working on playing out in their mind's eye as the first step. And to imagine the various parameters have sliders attached to them that one can adjust to get varying results. Really just to get them thinking about causal relationships and the parameter space in some way first, since that is what they are gonna be exploring as they solve the problem anyhow.
Sounds like an excellent approach. I think students end up getting a lot more out of a single problem from doing it this way because they start to think about the space of similar problems.
I might be a bit slow here, but I don't get your point at all.
More precisely, what is "conceptual prediction"?
What am I predicting? In what way? What is an example of a prediction?
Some kind of guess what the result will be? How would that help in any way?
Or does this specifically only have to do with some intuitive understanding of physical systems in physics?
A conceptual prediction is an educated guess about the outcome of a problem before you start any mathematical calculations. It's based on your understanding of the underlying principles and your intuition about how things work.
For example, in a physics problem about a falling object, you might predict that "the speed will increase as time passes." Or in a collision problem, you might predict that "momentum will be conserved."
@@kenny-iv5cq Sure, that is what he said. So it is not really trying to guess the outcome, like "I belive the answer will be 5", but rather thinking about the consequences of the assumptions before doing anything else.
The final outcome itself might be hard or almost impossible to guess, but general consequences of the assumptions like "the speed will increase as time passes." is of course useful to think about. I just think it was weirldy formulated if that was what he was after.
You don't try to guess what the final solution should be, you think about the consequences of the assumptions as much as you can. If in rare cases, this can help you guess the final solution, then fine.
that works btw. i used that approach to learn physics for my exam knowing nothing about physics. its like formal logic actually in a sense that you get factually correct statements and arrange them so that output of the whole thing makes sense
nice video, i would like to know your opinion abouth the ultralearning book
Damnnn i loooooove ya video
Sir kindly upload learning techniques for medical students pls
second this!!
Thanks for the video, really helpful stuff, I hope I'll remember to come back to this when the semester starts haha.
On a side note, I wanted to ask your perspective on 2 questions about practical implementation of learning science into real life.
I'm sure you're busy, so you can answer with as little or as much detail as you like (or maybe make a video if that seems like a good idea!).
1) Do you have a structure or recommendation for going about implementing all the ideas and advice in your videos into our personal, real life pursuits? Some sort of way to identify and adapt relevant theory to whatever thing we want to learn, whether it's physics, social skills, starcraft, stress management, or even something as simple as building an exercise habit? I guess you could call it meta-meta-learning haha.
Context: I find myself watching a lot of your videos and feeling very enlightened and captivated, but looking back I haven't actually put as much of it into practice as I'd like. I think it's because it can be difficult to know where to start, which concepts to apply where, and things like this.
2) What's your opinion on how emotional intelligence plays into learning, and how can you practically improve it to make the actual (and hopefully more effective) practice you end up doing in real life adaptable enough to your preferences and the follies of everyday life to be enjoyable and somewhat sustainable?
Context:
There's a lot of emotional traps that you can fall into while learning that can make it difficult to actually do practice that's more effective. Making practice plans but then procrastinating them or ending up "practicing" in more unhelpful ways because they're easier; feeling demotivated by failure or overwhelm about not knowing what's going wrong or what to do.
Things like these have been the other biggest obstacle in translating intriguing and motivating learning theory into actual improvement in my real life. As I've gotten better with EQ skills in other parts of my life, I've found myself (building practice plans that increase my chances of) following through more, but part of me again wishes I had a more structured way of improving on this, especially with respect to learning specifically.
Again, thanks for your videos, the things that I actually have implemented like free recall have been really helpful not just in my own studies, but also in tutoring friends and peers. I'm excited to see the videos coming next, and also your course!
this may sound unrelated but can you please make a video about what reading books actually does to brain that makes reading so important. I also wonder how reading improves problem solving skills, critical thinking etc.
Wow. Solving thousands of problems in physics without learning physics sounds a lot like the real life analog to overfitting, a concept in machine learning.
In machine learning it is common practice to separate the data out into multiple sets. A training, validation and test set. They're trained with the training set, of course, and during that time, their progress is assessed with the validation set. What this does is it prevents the model from essentially memorising the data points which wouldn't be really useful in the real world.
But the validation set also isn't perfect because it is used to inform the ML practitioner's decisions on how to train things, and eventually they might accidentally land on a model that miraculously understands the underlying pattern... but only for the training and validation sets. So you have to test that as well. Chances are because of the numerous iterations and tweaks from the practitioner, the models that seem the best are likely to have a positive bonus purely from chance.
They go through that much for an extra few percentages of accuracy.
Compare this to the education system which holds practice tests as bibles. "Guy's it's simple, when we give them practice tests, they do better!" There were literal companies coming in to give a basic talks on ways to increase your grades at my school. And it boiled down to taking practice tests. That was it...
Teaching to the test. Perhaps that's why the education system is so wanting - because they inform themselves based on the tests and only the tests. No question of true performance or understanding. This doesn't seem changeable because on which metrics do we evaluate a better education? It's like a misaligned AGI, the government. Can't tell it nothing except what will get it closer to it's predefined immutable goal.
I know it may be somewhat a stretch to say that they're teaching to the test, since the practice tests are actually different data-points, so to speak, to the real tests - but call them training papers and it's the same thing. The environment is the same, so you're not testing conceptual understanding necessarily, so much so as you are testing paper doing. And certainly papers have their patterns.
What are the traits of highly intelligent people? Can we train ourselves to be one of them?
It would be so interesting if you take these questions as an idea for next video!!!
Info of the particular physic book?
Can an example be given? Like for something like linear algebra, or programming/algorithms? I am not sure how to apply that step..
🎉😊
In the three-step process, step 3 is Profit! I learned that from South Park.
Your videos are absolutely awesome! Have you heard much about the "ecological approach" to coaching/teaching/learning? As opposed to teacher-directed learning? Seems to contain some very interesting ideas, would love it if you made a video exploring the ideas!
(An Ecological Approach to Empower the Learner, Martha W. Bristor and Dorothy L. West)
Thanks! I have read some theoretical accounts of the ecological approach, but would want to do more reading before I made a video on it. In general, I'm a fan of more "systems-like" thinking for social situations (like learning). There are times, though, when the practical importance of the shift in thinking escapes me.
@@benjaminkeepThanks for your response! How about for learning sports? The way I heard of it first was via a jiu-jitsu coach who takes a very different approach to the traditional sequence oriented coaching: czcams.com/video/V4QtQTRwwD0/video.html
His athletes have performed very well recently which has also put this way of coaching the sport on the map somewhat. Food for thought
Its 7, because 173 is prime, 17 is also prime, as is 73, and 13
Is this only applicable to learning physics
or also applicable to subjects like law?
It seems to me that it should be applicable for anything that would involve developing a general understanding as well as using routine procedures to solve problems.
I wonder if he likes Go...
You are a real saviour, could you make a video dicussing how "geniuses" become genus"
I don't wanna learn during an exam 💀
By "YouSum Live"
00:02:00 Importance of conceptual predictions before executing solutions
00:02:20 Evaluating answers based on conceptual predictions
00:03:36 Significance of comparing conceptual and numerical solutions
00:04:00 Learning from conflicts between conceptual and mathematical models
00:04:41 Improvement through understanding conceptual predictions
00:05:00 Integration of conceptual predictions and mathematical skills
By "YouSum Live"
GUESS: Givens, Unknowns, Equations, Substitute, Solve ❌
GUESGS: Givens, Unknowns, Equations, Substitute, Guess, Solve ✅
Guess Inception
I see a danger there. Making a prediction may influence equations to solve the execution -- in a bad way.
But the thing about physics is that you have the answer of the textbook. So you can see if you are wrong in your predictions and them, after a lot of these wrongs and errors, you can steer your intuition and make better predictions. The problem would lie if you had no way of knowing whether you are right or wrong.
Are you deliberately slowing down the last two words of many sentences so you can sound….
like….
Obama… ?
nice video, i would like to know your opinion abouth the ultralearning book