One hundred Hyde & Rugg articles, and the Verifier framework

By Gordon Rugg

This is the 100th post on the Hyde & Rugg blog. We’re taking this opportunity to look back at what we’ve covered and look forward to what comes next.

The image below shows some of the main themes and outputs so far, in the “knowledge cycle” format that underlies our Verifier framework for tackling human error. If you’ve come to this blog after reading Blind Spot, you might be pleased to discover that we’ve been covering the contents of Verifier here in more depth than was possible in the book, and that we’re well on the way to a full description.

In the image below you can see some of the main themes and topics we’ve covered so far in the “knowledge cycle” format that underlies our Verifier framework for tackling human error. If you’ve come to this blog after reading Blind Spot, it’s worth knowing that we’ve covered some of the the contents of Verifier in more depth here than was possible in the book, and that we’re well on the way to a full description.

The knowledge cycle, and topics that we’ve blogged about

overview and hundredth articlev2Copyleft Hyde & Rugg 2014

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How complex should education theories be?

By Gordon Rugg

There’s an old joke in the physical sciences, often attributed to Einstein, that a model should be as simple as possible but no simpler.

The converse is that a model should be as complex as necessary, but no more complex.

In this article, I’ll discuss what the most useful level of complexity is for education theories.

golden gate fogv1Clarity emerging from the fog: Cropped image from wikimedia

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Compiled skills and education theory

By Gordon Rugg

In a previous article, we described a framework for mapping different types of knowledge (in the broadest sense) onto different methods of teaching, training and learning (also in the broadest sense).

https://hydeandrugg.wordpress.com/2014/04/13/an-education-framework-based-on-knowledge-modelling/

That article was a broad overview. This article shows a worked example of how the framework operates for one category from the framework, namely compiled skills.

verifier educationv2Image copyleft Hyde & Rugg, 2014

Compiled skills are a type of strictly tacit knowledge that have traditionally been viewed in the education world as something of a black box. They are particularly problematic for some views of education because their performance is usually adversely affected, or completely disrupted, by any attempt to verbalise them. For any sport enthusiast, they are a familiar phenomenon, usually under the name of “the flow” or of “muscle memory”.

This article unpacks the nature of compiled skills, and examines the implications for education theory and practice.

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An education framework based on knowledge modelling

By Gordon Rugg

Education is about getting new content into student’s heads, via some combination of teaching and learning.

In order to do this in an evidence-based way, one key element is a solid categorisation framework for each of the variables involved. Three key variables are:

  • Types of content
  • Types of delivery
  • Types of learning

There are other important variables, such as physiological constraints, but we’ll focus for the moment on the three listed above.

Existing educational categorisations, such as the Visual/Auditory/Kinaesthetic model, tend to be unsystematic and/or very coarse-grained. In order to handle this area properly, a category system should as a minimum be able to handle systematically the types of content and of delivery and learning shown in the diagram below, and preferably be able to handle more.

verifier educationv2

This article is a brief overview of how we have been tackling this issue. We will go into more detail in later articles.

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Teaching the facts

By Gordon Rugg & Sue Gerrard

There’s a lot of debate in education about “teaching the facts”.

There’s also a lot of debate about the definition of “facts” and about the nature of teaching.

However, a couple of things tend to be conspicuous by their absence in these debates.

  • There’s a significant absence of numbers relating to facts, such as how many facts a student should know about a particular topic.
  • There’s also a significant absence of categorisation systems that use more than three categories.

These absences are usually indications that a debate is focused on questions that aren’t going to produce useful answers.

So what happens when you plug in some numbers, and some richer categorisation?

In brief, you get this:

  • students need to learn between one thousand and ten thousand facts
  • there’s an upper limit of learning of about ten facts per hour, and
  • you need to distinguish between about ten to twenty different types of “fact”.

These results have far reaching implications for education. They’re the topic of this article.

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