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IDEA: A series of nonverbal algorithm assembly instructions

idea-instructions.com

1–9 of 9 posts

Re: IDEA: A series of nonverbal algorithm assembly instructions

#3
post #2

While it's an interesting idea, these are confusing to understand even for someone who knows these concepts.

It seems possible that having to puzzle them out makes it an effective way to learn, at least if you can stand to.

Re: IDEA: A series of nonverbal algorithm assembly instructions

#5
As a visual learner (even when learning a new word or the name of a new acquaintance, I must picture how the letters look standing next to each other, like a little painting, or I do not remember it), I actually find this way of learning an algorithm fun and engaging - it's what I end up having to do in my head anyway, and I may not picture the system as correctly as the instructors who drew up these instructions - which will inevitably lead to a TA telling me how I have the "wrong intuition" about a particular theorem when I seek help...

I just understood Fleury’s algorithm in about 5 minutes - I cannot tell you how fast and revolutionary that is for me.

Re: IDEA: A series of nonverbal algorithm assembly instructions

#9
I think the idea can work, however the execution is still far from perfect.

Just on the binary search for example, the "balance" representation is re-used to signify the concept of "comparison", however the tokens put in balance are both of the same size, with only an image to differentiate them. So the link is not implicitly made from their size, weight, and the intrinsic feature which makes the one searched token the right one. This would be confusing for anyone trying to understand the algorithm.

Then, you got a first depiction of the problem, with each object being ordered by size. So the initial assumption is that the choice element would be on their size. Afterward, the target token has a star on it, and the generic, incorrect one, a question mark.

That could work, however, they both have the exact same size.

This is only an example of how the concepts used to explain and signify things visually are only partially followed-through. This greatly impairs the impact of the explanation.

And this is only on the binary search, arguably the simplest algorithm depicted here.