Exercise for everyone to do

Greetings from India. Here’s homework for everyone to do.

Here’s the Jeanette Wing article on computational thinking:

How does this relate to the Kirschner et al article that @neeldhara posted last week?


An Excellent article!

Thank you for sharing. It’s a very interesting read.

Folks, I don’t think I was clear so let me say it in more detail.

Your task is to read the Wing article, then analyze it through the lens of the Kirschner article that @neeldhara posted a few days ago.

The two are deeply connected, and the connection is very important to understanding the article.

Please don’t just comment that it’s very nice, etc. You need to analyze it.

This is preparation for the workshop you have signed up to attend!

I’ll bite, just a bit. According to Wing, as we go about our everyday lives, we’re probably processing information in a manner that is not unlike how software programs would when tackling traditional computational problems. For example, if I have a pack of playing cards that I am trying to put back in order, I am probably bucket sorting, or something like that.

The transfer article, on the other hand, appears to be about how having acquired skills in a certain set of tasks does not automatically imply an advantage in any mostly-unrelated scenario (but still having a vague enough connection that policy makers may come to the opposite conclusion regardless).

I don’t see a direct connection between the two articles[1]: Wing doesn’t seem to be claiming that people who are good at sorting their playing card decks will do well in the classroom when asked to study and compare sorting algorithms. My main takeaway from the CT article is simply that computational thinking involves more than just learning programming syntax, and there seems to be some advocacy for introducing materials that encourage a certain kind of thinking.

I don’t think there is express hope that because such thinking shows up in everyday life, a course on ‘how to think like a computer scientist’ will be automatically easy/relatable/etc. I am not sure why the everyday life anecdotes are there: my best guess is that it is to convince policymakers that these ideas are useful.

The “there is more to CS than learning to code” felt a lot like Lockhart’s “there is more to math than formulas”. I didn’t read anything more into Wing’s viewpoint, but looking forward to other people making the connections that I missed :slight_smile:

  1. potentially adding to the sea of evidence that transfer is hard :grinning: ↩︎

I think that is too simple a reading of Wing’s article. If all she wanted to say is “there’s more to CS than programming”, she would not be talking about backpacks and mittens and choosing a line in a supermarket.

Well, indeed, I did confess to not being sure about why those examples are so elaborate:

I am not sure why the everyday life anecdotes are there: my best guess is that it is to convince policymakers that these ideas are useful.

OTOH, I did not find explicit claims of transfer either :woman_shrugging:

Wing’s article talks about backpacks and mittens and so on which are near transfer situations to CS problems. Maybe if students are able to figure the identical elements (or if the instructor introduces these identical elements) then they might do better in CS problems. But later she talks about:

One can major in computer science and do anything.

This is a far transfer and as per Kirschner article and Thorndike’s revised claims, there is no strong evidence for far transfer unless there are many identical elements. So, teaching/learning CT does not necessarily mean one can do anything (as stated by Wing). So we should treat the last few para’s of Wing’s article with caution ?!

This is my understanding. I still do not understand the Kirschner article very well. Same doubt as @neeldhara and have posted in the other thread and happy to get it clarified here or at CRiCKET.

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True that they are near transfer situations, but was there a claim that these transfers happen? I thought these examples are just brought in to establish that ideas taught in CS courses are universal and manifest often in the real world, and I simply saw it as an advertisement, broadly, of the utility of the topics taught in a typical algorithms course.

I often say things like algorithms run our world, and I don’t mean to say that “our experience of the world around us helps us learn algorithms faster” or anything like that.

Ah… I see. My interpretation of this was that as a subject, CS is ubiquitous in the sense that math is: so if you do pick up a domain specialization after a CS degree (e.g, in law or medicine), the CS training is likely to be useful — not in the sense of transfer (i.e, that CS know-how makes it easier to understand nuances of law or medicine), but in the sense that it is very likely that you will be solving problems where the CS training simply helps make progress because those problems are likely to have actual CS-ey components to them.

@Partha, you’ve read this exactly right.

I don’t think even the “near” transfers are actually all that near.

I have some really good slides prepared on transfer that I can give at CRiCKET. You would be shocked by some of the issues that arise.


Yes, but “algorithms run our world” is just a statement about the world — it’s not a statement about learning or the ability to turn learning into action. Yet that’s what Wing’s article is about. The word “thinking” is in the very title; that means it’s about humans, not about machines or algorithms.

You may have a narrow sense of what you mean when you say “the CS training is likely to be useful”, but many people have a much more expansive sense, which is Wing’s sense.

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I do not think these two articles are related in any discernable way.
I read this CT article again, and it hasn’t changed my view about the article: it is a marketing article that creates an awesome-sounding phrase and labels every imaginable problem-solving activity and approaches with it. I would have expected it to start from a definition of what Computational Thinking is, and maybe compare and contrast it with other types of thinking - critical, creative, philosophical, analytical, etc.

I do buy the point that the ability to factor in computability of a problem (and how well computing can be leveraged to solve the problem) is a skill worth having for a large number of people. But I am not sure if this is a type of thinking. But then since this article created a revolution of sorts, so I may be missing something fundamental.

Transfer of learning (as discussed in Kirschner article) - I think @neeldhara and @Partha have good discussion on this topic and how it applies to CT (and not). If I do try to force a relation, here is my take:
CT seems to argue that CT is a horizontal skill and is applicable at a lot of places, like a maths or language skill is. Math or language doesn’t work because there is transfer learning involved, they work because they give us a language to think, analyze and communicate in. Computing does give a vocabulary - as the article suggests (filling bag is like pre-fetching, trying to search a lost item is backtracking, which I thought were pretty cheesy examples!) - CT co-opts this, and in that sense only provides a tool to the learner to do some tasks more easily, there is no notion of transfer here.

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