Finding the right references, part 3: Breadth, depth and the T model

By Gordon Rugg

In the previous article in this series, I looked at ways of getting a mental overview of the key concepts in an area.

In today’s article, I’ll look at how to decide which are the core articles that you need, in a way that should be swift, simple and manageable.

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Guest Post: Representing lesson structure graphically

By Gavin Taylor

Lesson structure can be seen as a core aspect of teaching; the method in which lessons are planned can influence the whole learning process. Most teachers plan the structure of their lessons using a few well established techniques. One is a three level approach commonly known as a traffic light sequence, as shown below.

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This traffic light system can be used for assessing pupil progress and for differentiation of tasks, as well as clearly showing the lesson structure. This system however has various limitations. For example, this system implies that unless a pupil “moves” from one colour to another, progress has not been made, even though the pupil’s understanding may have been deepened. The criteria for progress also have to be correct; a pupil could, for example, achieve the red objective in the figure above without completing the amber, as these may not be progressive objectives.

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Finding the right references, part 2: Getting a first overview

By Gordon Rugg

In a previous article, I looked at some issues that affect how and why finding the right academic references can be difficult. In today’s article, I’ll look at how to set about finding those references, beginning with the familiar problem of reading lists.

Some lecturers supply reading lists; others don’t.

Reading lists can be very comforting, because someone else has already done the thinking for you, and has told you what you need to know. There’s also the nice implicit message that you only need to know what’s on that list, so there’s a limit to the work ahead.

It’s a comforting feeling while it lasts, but there’s usually a small voice at the back of your mind asking what will happen when you leave university and enter a world where nobody is likely to give you a reassuring list of the things that you need to know.

Which brings us back to the lecturers who don’t supply reading lists, and who expect you to find information for yourself, with only a few words of guidance, such as “Weber’s work on bureaucracies is the place to start” or “Good question; that’s a classic sociotechnical problem”.

Where do you go from that sort of start? Your friendly librarians will usually be very happy to give you good advice about how to locate information online, and often, that advice will find you exactly what you want. However, there are some other quick and dirty methods that you might find useful. They’re what this article is about.

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Finding the right references, part 1

By Gordon Rugg

The best questions are often short.

In a comment on a recent article here, Mosaic of Minds asked which authors I’d recommend for further reading about Likert scales. It’s a fair, sensible question, which lifts the lid on a whole boxful of issues about academic references. Many of those issues are important, but not as widely known as they should be.

This article is the first in an informal series about academic references, online search, and the ways that evidence is used in research. In this article, I’ll be looking at two concepts that provide some useful structure for understanding this general area, namely craft skills versus formalised knowledge, and back versions versus front versions. I’ll start with an overview of these concepts, and then look at the insights they give into different sources of information, including academic references.

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Likert scales and questionnaires

By Gordon Rugg

I really, really, really hate badly designed questionnaires.

That’s an issue, because most questionnaires are badly designed. The bad design makes them worse than useless. At least if something is useless, it isn’t making the situation actively worse. Badly designed questionnaires, however, can make a situation significantly worse, by adding disinformation into the story, so that a problem takes longer to solve.

This is even more of an issue because questionnaires are so widely used. Any idiot can design a bad questionnaire, and many idiots do, with a variety of excuses, such as:

  • Every other idiot is doing this, so I want to get in on the act
  • Nobody ever got fired for using a questionnaire
  • It’ll all come right in the end anyway, even if I do it badly
  • Who cares?

None of these arguments inspire much confidence or respect with regard to the person using them.

In this article, I’ll make a start on the issues affecting questionnaires. They’re big issues, that have deep roots and broad implications, so discussing them in full will take a number of articles. For now, I’ll focus on a single topic, namely how Likert scales can be used within questionnaires.

Likert scales, and Likert-style scales, are widely used (and widely misused) in questionnaires. In this article, I’ll look at some of the key concepts involved, and at some of the issues involved in using this approach properly.

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Timelines, task analysis and activity sequences

By Gordon Rugg

This article is a re-blog of part of a previous article about assessing whether or not you’ve met a client’s goals in a product design.

I’ve re-blogged it to form a free-standing article, for anyone interested in systematic approaches to recording and analysing people’s activities. I’ve lightly edited it for clarity.

The examples I’ve used below relate to product evaluation, but the same principles can be applied to other human activities, such as how people make decisions when shopping, or how people find their way around in an unfamiliar place.

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Life at Uni: Why is my timetable a mess?

By Gordon Rugg

Every year, huge numbers of new students start university, and are surprised to discover that their timetable is very much a work in progress (and sometimes, a work of fiction). Every year, understandably, huge numbers of new students react to this discovery by wondering why universities crammed with alleged geniuses can’t sort out something as simple as a timetable. It’s not an encouraging start. This article is about the reasons for this state of affairs.

The main reason is that timetabling actually isn’t simple. In reality, it’s hideously complex. The timetable for a single university has to handle thousands of students, hundreds of modules, hundreds of academic staff, and hundreds of rooms. Very few of those students want lectures first thing in the morning or last thing in the afternoon, or on a Monday or Friday, so some slots are much more in demand than others.

Reconciling all of these issues is a huge, messy problem, but it could in principle be resolved by using smart software; some universities already use cutting-edge software that can perform impressively well, if other things are equal.

Unfortunately, the big spanner in the works is that other things usually aren’t equal. Here’s a classic example of why timetables are often fluid until well after the first week.

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