Mental models and metalanguage: Putting it all together

By Gordon Rugg

The previous articles in this series looked at mental models and ways of making sense of problems. A recurrent theme in those articles was that using the wrong model can lead to disastrous outcomes.

This raises the question of how to choose the right model to make sense of a problem. In this article, I’ll look at the issues involved in answering this question, and then look at some practical solutions.

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Pattern matching

By Gordon Rugg

Note: This article is a slightly edited version of an article originally posted on our Search Visualiser blog on May 17, 2012. I’ve updated it to address recent claims about how Artificial Intelligence might revolutionise research.

So what is pattern matching, and why should anyone care about it?

First picture: Two individuals who don’t care about pattern matching (Pom’s the mainly white one, and Tiddles is the mainly black one (names have been changed to protect the innocent…)

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Pattern matching is important because it’s at the heart of the digital revolution. Google made its fortune largely from the simplest form of pattern matching. Computers can’t manage the more complex forms of pattern matching yet, but humans can handle them easily. A major goal in computer science research is finding a way for computers to handle those more complex forms of pattern matching. A major challenge in information management is figuring out how to split a task between what the computer does and what the human does.

So, there are good reasons for knowing about pattern matching, and for trying to get a better understanding of it.

As for what pattern matching is: The phrase is used to refer to several concepts which look similar enough to cause confusion, but which are actually very different from each other, and which have very different implications.

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Why are we being examined on this?

By Gordon Rugg

It’s a fair question, if it’s being asked as a question, rather than as a complaint about the cosmic unfairness of having to study a topic that you don’t see the point of. Sometimes, it’s easy to answer. For instance, if someone wants to be a doctor, then checking their knowledge of medicine is a pretty good idea.

Other times, though, the answer takes you into deep waters that you’d really rather not get into, especially if there’s a chance of some student recording your answer and posting it onto social media…

Why do some answers take you into deep waters? That’s the topic of this article. It takes us into history, politics, proxies, and the glass bead game.

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Truthiness, scienciness, and product success

By Gordon Rugg

This is a Tempest Prognosticator.

800px-Tempest_Prognosticator“Tempest Prognosticator” by Badobadop – Own work. Licensed under CC BY-SA 4.0 via Wikimedia Commons – https://commons.wikimedia.org/wiki/File:Tempest_Prognosticator.jpg#/media/File:Tempest_Prognosticator.jpg

It’s a splendid example of nineteenth century ingenuity, right down to the name. What does it do? It’s intended to let you know if a storm is approaching. The way it does this is as wonderfully nineteenth century as the name. The Prognosticator is operated by twelve leeches, each of which lives in a bottle. When storms are approaching, the leeches become agitated, and climb out of the bottle. When they climb out, they disturb a piece of whalebone, which activates a bell. The more serious the risk of storm, the more leeches climb out, and the more bells ring.

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Passive ignorance, active ignorance, and why students don’t learn

By Gordon Rugg

Why don’t students learn?

It looks like a simple question, which ought to have a simple answer. In reality, understanding why students don’t learn takes us into concepts such as passive ignorance, active ignorance, systems theory, belief systems, naïve physics and cognitive biases. In this article, I’ll skim through some key concepts, to set the scene for a series of later articles that go into more depth about those concepts and about their implications both for education and for other fields.

bannerBucket image copyleft Hyde & Rugg, 2014; other images from Wikimedia (details at the end of this article)

 

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Literature reviews

By Gordon Rugg

Almost every academic article begins with a literature review. As is often the case in academia, this is a rich, sophisticated art form, whose complexities are often invisible to novices. As is also often the case in academia, there are usually solid, sensible reasons for those complexities. As you may already have guessed, these reasons are usually not explained to students and other novices, which often leads to massive and long-lasting misunderstandings.

This article looks at the nature and purpose of literature reviews. It also looks at some forms of literature review which are not as widely known as they should be.

It’s quite a long article, so here’s a picture of a couple of cats as a gentle start.

cats on sofa

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Trees, nets and teaching

By Gordon Rugg

Much of the debate on education uses diagrams to illustrate points being discussed.

Many of those diagrams are based on informal semantics.

The result is often chaos.

In this article, I’ll use the knowledge pyramid as an example of how informal semantics can produce confusion rather than clarity.

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