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On Monday I attended the CLOSER workshop on lifecourse research. The presenters were largely from the health care world such as epidemiology and psychiatry which was refreshing because so far in my education on quantitative lifecourse research I have mostly been exposed to economists. Perhaps it is because people working in health talk about death and disease, something we all can relate to, I found that I didn’t actually get bored once and doodle like I sometimes do when economists present (my fav doodle is of Bart Simpson – quite hard to draw him just from memory as you can see):

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Bort Sampson

What kept my attention I think, was the wide variety of ways the data was presented. They used graphics, diagrams, flow charts and graphs and only some equations.

As I write this I am enjoying some peaceful time in the British Library and just went to see the Beautiful Science exhibition. Interestingly, many of the leading people in data visualisation from the past seem to have been involved in health (as well as cartography and biology), so perhaps the display of data is very much part of epidemiologist’s history and culture.

I do think that you have to be careful with visuals though. The tree as a visual metaphor I think has been a bit overused for example. Here is the tree that started it all from 1879:

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You’ve got a lot to answer for Ernst Haeckel

So you shouldn’t just do something graphical because you think it looks nice. It has to actually be meaningful.

Although perhaps I can and integrate my Bort Sampson pictures into my thesis somehow though as I think it can help me say something meaningful about schooling and education?

(c) 2014 Annika Coughlin

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In 1996 I was a school girl studying for my GCSEs. My older sister who was two years older had left school with below a C grade in Maths. She had to retake at sixth-form college but ended up with a lower grade than her first attempt and on her final retake scored ‘Ungraded’. The reason for this decline in motivation and increased boredom I witnessed with each retake was a bit of a worry for me as I didn’t want to go through this.

So I set myself up a little revision room in the watertank cupboard at home (it was the only ‘spare room’ we had). I had a desk, a chair, a radio/cassette player and made a detailed colour coded GCSE revision timetable. As you tend to do about 10 GCSE exams at the same time I had to prioritise time spent on subjects. So maths got top priority, marked in yellow. I worked hard revising for a couple of months and despite the watertank being hot and noisy, especially at bath times, my revision schedule worked a treat and I got a C!

Now that I have elected to do Advanced Quantitative Methods as part of my PhD I have to revisit some GCSE maths as I feel that I have lept right into a whole advanced world before I have mastered the basics. This became very obvious to me last week when in our statistics class, the lecturer said ‘now here is a quick recap on logit which you would have done at school’. There is NO WAY I did this at school.   This logit business is just for the people in the advanced sets who used scientific calculators and not something we lower sets would have been burdened with. But the fact that the lecturer assumed that we had all done this before made me realise that it must be quite unusual for someone from the lower sets to even think about trying out Advanced Quantitative Methods in their future lives.

However, I am a firm believer that your GCSE grades don’t really reflect much – they reflect how good your teacher was, whether or not an annoying boy pulled at your hair all lesson, as well as how much you revised in the watertank cupboard in the summer of 1996, but they don’t reflect current and future potential to learn.

So in the spirit of my PhD topic which is about lifelong learning, I would say do not let your past experiences of school maths stop you from learning now – start off by using the marvellous online tutorials which of course did not exist back in 1996, such as the Khan Academy (especially the algebra stuff which is essential for regression statistics). And if you quite liked the old maths book you used in the past, you might be lucky to find it in the IOE library which has a collection of ‘retro’ GCSE textbooks. I found my old watertank cupboard friends:

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Retro maths books: Let’s convert those francs to pounds!

(c) 2014 Annika Coughlin

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