If You Learn A, Will You Be Better Able to Learn B?

:globe_with_meridians: Link: If You Learn A, Will You Be Better Able to Learn B?

:open_book: Summary: This is a short article on Understanding Transfer of Learning By Pedro De Bruyckere, Paul A. Kirschner, Casper D. Hulshof.

See also: this related talk.

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Nice try! I started reading, didn’t feel short at all, and had to leave mid-way!
Good article though, worth reading in full.

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EVERYONE: read it in full. It’s a CRITICAL article for everyone to read, especially those who believe in religious concepts like “computational thinking”.

Set time aside to read it properly. I will quiz you on the article at CRiCKET.

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Neat article @neeldhara! Thanks for posting it.

It gave a similar vibe to a couple of books I read long back unrelated to computing and more related to learning. One is a really popular book “Thinking, fast and slow” by Daniel Kahneman and “Range” by David Epstein.

Both had similar anecdotal stories about chess and music. I don’t remember latin but yeah the idea that one is good at something specific doesn’t impact any other skill. But I do think there are certain elements which help. Interdisciplinary subjects come out of these varied interests.

Videogame development for example is result of being a good developer + digital artist + storyteller.

It’s more of a or situation than an and situation and one can use 3 different specialist but it helps to have some intersection. But that’s anecdotal I don’t think it helps empirically.

@shriram I didn’t know much about computational thinking; reading the wikipedia. It feels similar to other thinking/design principles like clean code etc.
Highly subjective but for whom it works they evangelize it.

I might be wrong but I’ve never found a silver bullet to teaching a sub topic in computer science :sweat_smile: or designing software.

Let’s tease apart all these things, folks. It doesn’t help to conflate everything with everything else.

Kahneman’s book is about the cognitive response system and about heuristics that we use for evaluation. It is not at all related to transfer. There’s nothing about transfer that says it has to happen instantaneously. Those books may contain stories about chess or music (I don’t know the Epstein book) but they are not related stories.

“Computational thinking” is not a design principle nor a solution approach. Therefore, it’s a type error to even think of it as an approach that “works for some people” or as something that can or can’t be a “silver bullet”.

Let’s think precisely, otherwise we’re going to get overwhelmed by the many concepts we’re wrestling with here and eventually end up talking completely at cross-purposes. The article Neeldhara posted is about a very specific set of claims that infuse educational thinking in general and computing education in particular. Let’s just focus on understanding it in its own right.

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:sweat_smile: I didn’t want to conflate the ideas. The books were tangential suggestion to article’s vibe.

I am still reading about Computational Thinking

And since you mentioned it’s a religious concept I thought it was similar to design principles that I have read.

So I mentioned clean code because most of the problems it tried to solve I cannot use outside of Java or Java’esque programming language.

There are some general vague ideas that are helpful but nothing specific.

So transfer of skill, which I wanted to highlight, was using ideas which work in one programming language and transferring to other languages.

So near transfers like conditions, loops, variables are some things that work well and far transfers are design principles which often fail to transfer beyond some general ideas.

I think that’s what I felt as soon as I read the article.

Sorry if I wrote it in a bad way and completely missed the point I was trying to make :see_no_evil:

I’ll read on computational thinking and try to see what’s it’s appeal.

Transfer between programming languages is certainly an interesting idea, and one that has not been studied very much. We actually wrote a paper about this this past year, and the related work will provide citations to other work that has looked at this question:

https://cs.brown.edu/~sk/Publications/Papers/Published/lkft-switch-fun-lang/

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Oh that’s cool! This would be a fun read. Thanks!

Just to be sure, is the following what we mean by computational thinking?

Computational Thinking is the thought processes involved in formulating problems and their solutions so that the solutions are represented in a form that can be effectively carried out by an information-processing agent.

source

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That’s one of several definitions. People love to come up with new definitions for it, which is telling. (-:

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I see. Which one do you have in mind when you refer to it?

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Nice article. Thanks for sharing

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I have more pointed things to say, but on a first reading, I can’t help but admire (and chuckle) at the similarity between (the disappointment in the conclusion of) this article, and (the driving curiosity behind) the article Can a biologist fix a radio?—Or, what I learned while studying apoptosis by Yuri Lazebnik.
It’s almost like they’re arguing in reverse.

The original article begins by examining claims of far transfer, and concludes in disappointment:

In this article, we investigated four popular examples of claims for far transfer, but in each case the results were disappointing. This is not to say that there is no evidence whatsoever for far transfer, but it’s very clear that the level of reliable evidence decreases in relation to the quality of the research: the better the research, the scanter the evidence.

Lazebnik begins by investigating a similar frustration (which he attributes to increasing complexity):

At some point, David said, the field reaches a stage at which models, that seemed so complete, fall apart, predictions that were considered so obvious are found to be wrong, and attempts to develop wonder drugs largely fail. […] In other words, the field hits the wall, even though the intensity of research remains unabated for a while, resulting in thousands of publications, many of which are contradictory or largely descriptive. […] This stage can be summarized by the paradox that the more facts we learn the less we understand the process we study.

And Lazebnik’s solution to the paradox? Is a claim of a (far?) transfer :laughing: :

However, I hope that it is only a question of time before a user-friendly and flexible formal language will be taught to biology students, as it is taught to engineers, as a basic requirement for their future studies.

Ouroboros.

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I usually refer to the original Jeanette Wing one, because that’s the one that most cite, and one that many implicitly believe. (And also the one that is easiest to show as wrongheaded.)

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The biologist-radio article is a favorite of mine; thanks for bringing it up. The proposed solution is especially funny in this context. But note that the solution always involve bumping the problem off to someone else! And in this case, to us, the formalizers of language. (-:

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Though as a rank outsider, I don’t see that the systems biology approach has had a lot of huge successes yet. Maybe it just takes decades to build up the formalisms to match 5 billion years of evolution.

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Convenient much?

*ducks*

Only reason I asked is so we all know what we are talking about and not talking past each other — the phrase does seem to mean different things to different people.

The paradox reminded about something Gladwell mentioned. I don’t know if there are any empirical evidences to back this claim but it’s an interesting statement.

A puzzle can be solved by obtaining more, factual information, whereas a mystery requires judgements and the assessment of uncertainty, usually in the face of too much information.

I don’t know if there is any truth to it but that’s what I remembered.

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Haha. I partly say it’s easy because it’s at least refutable or contestable, unlike some suggestions that are so fuzzy as to be beyond that.

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Nice article, thanks for sharing.
I liked the part that discusses about programming and problem solving the most.

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