By Gordon Rugg and Sue Gerrard
This is the third post in a short series on semi-tacit and tacit knowledge. The first article gave an overview of the topic, structured round a framework of what people do, don’t, can’t or won’t tell you. The second focused on the various types of do (explicit) and don’t (semi-tacit) knowledge. Here, we look at can’t (strictly tacit) and won’t knowledge.
The issues involved are summed up in the diagram below.
By Gordon Rugg and Sue Gerrard
Tacit knowledge is knowledge which, for whatever reason, is not explicitly stated. The concept of tacit knowledge is widely used, and has been applied to several very different types of knowledge, leading to potential confusion.
In this article, we describe various forms of knowledge that may be described as tacit in the broadest sense; we then discuss the underlying mechanisms involved, and the implications for handling knowledge. The approach we use derives from Gordon’s work with Neil Maiden on software requirements (Maiden & Rugg, 1996; reference at the end of this article).
In brief, the core issue can be summed up as whether people do, don’t, can’t or won’t state the knowledge. If they do state it, it is explicit knowledge, and can be accessed via any method. If people don’t, can’t or won’t state the knowledge, then it is some form of semi-tacit or strictly tacit knowledge, which can only be accessed via a limited set of methods such as observation, laddering or think-aloud. Because of the neurophysiological issues involved, interviews, questionnaires and focus groups are usually unable to access semi-tacit and tacit knowledge.
The image below shows the key issues in a nutshell; the rest of this article unpacks the issues and their implications. There are links at the end of the article to other articles on the methods mentioned in the table. The image below is copyleft; you’re welcome to use it for any non-commercial purpose, including lectures, as long as you retain the coplyleft statement as part of the image.
By Gordon Rugg
So what is creativity, and how can you generate more and better ideas?
There’s pretty general agreement that:
That’s a good start.
However, when you start asking about how creativity works, or just how you’re supposed to think outside the box, or think laterally, an element of vagueness starts to roll in, like a dense bank of fog off the Atlantic at the start of a horror movie…
You start hearing stories of people and organisations that thought successfully and laterally outside the box, in a way that solved their problems with designing better elevators. You encounter puzzles involving people and items being found in improbable situations, such as stabbed to death with no weapon visible, in the middle of a field of unsullied snow. It’s all very edifying and interesting, but it doesn’t get to grips with what creativity really is, or how to do anything systematic about creating new ideas.
This article gives a brief overview of a systematic framework for making sense of creativity, and for choosing appropriate methods for generating new ideas.
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 might be for education theories.
Clarity emerging from the fog: Cropped image from wikimedia
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:
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.
This article is a brief overview of how we have been tackling this issue. We will go into more detail in later articles.
hyde and rugg 