I've been doing a fair bit of literature review work the last couple of days, mostly for some educational research papers I'm writing up. It's kind of interesting that completely by accident I decided to try out some collaborative small-group work stuff in my units this semester, when it is just about to become a focus (well sort of) within science and tech as well as the engineering faculty at QUT.
We are building this huge new science and technology precinct which is supposed to feature some so-called new age teaching and learning spaces (think new GP library, new S block levels, new O303a). Lot's of collaborative workspace, flexible room setups and (re)movable furniture. etc. When I saw the new level 4 S block lecture room with the glass walls, I wasn't impressed...in fact, I'm still not. It actually isn't any different so far as I can tell, besides the glass walls, from what it was when I was an undergrad 12 years ago. The furniture may even be identical.
But that's not the point... the point is I finally thought a little bit about what else I could do in their besides standing up and talking to people doing examples. The room in question has those large two-student flat desks that you can move around just like any old table. So I thought, bugger it, I'm going to get people to talk with each other and work on problems on purpose (rather than because that's what they decide to do). Another fortuitous (or not) thing was the closure of the students work room in O block. There wasn't really as easy an opportunity any more for students to just sit around and work on their maths with each other. In a sense, I tried to mimic that atmosphere, probably not very successfully, in my collaborative classroom activities.
Personally, I think we over lecture. 3 hours a week isn't really necessary. That doesn't mean I don't want to spend the 3 hrs teaching or in the classroom, but just that I don't think I need to be talking up the front all the time. I think in many cases, a 1 hour overview is more appropriate and then the remaining 3 hours could be spent in structured, small-group learning activities. What do I mean by structured? Well, not just a sheet of questions... more like worksheets that guide the students through constructing the ideas for themselves. You can't go too far off track because the guiding worksheet always brings you back to the path, but at the same time you would need to draw on your prior knowledge to figure out where to go and what to do.
This is also, in a sense, what I tried out with my collaborative activities. A couple of them were actually used as the introduction to a topic. THat is, I didn't teach the topic first, I got students to work on a structured, small-group worksheet. That probably seems weird and unfair...but it's actually quite good I think. It allows each student to build their own concepts and to arrive at a place in the progression of learning about something before I come in and sterilise their viewpoint with that of the so-called expert. I think it is even empowering to some people when they realise they can learn by themselves and that anything I provide is just reinforcement or guidance.
Saturday, November 6, 2010
Thursday, November 4, 2010
Day 16
Day 16 Part 2
QUT Beamer template [zip download]
So as promised, here is the first version of the QUT beamer template I put together with the help of my wonderful wife. Updates are sure to follow as it is pretty skeletal. Let me know if there's anything you suggest...I'll get around to it eventually.
QUT Beamer template [zip download]
So as promised, here is the first version of the QUT beamer template I put together with the help of my wonderful wife. Updates are sure to follow as it is pretty skeletal. Let me know if there's anything you suggest...I'll get around to it eventually.
Day 16 - AIM proposal
So, many years ago (ok 5) I went to a research workshop at a place called the american institute of mathematics (http://www.aimath.org). The workshop was on modelling cancer immunology and stuff like that and was organized by prof lisette de pillis (I did my postdoc with her math @ hmc) and some other folk (amy radunskaya and chuck wiseman). There were about 30 people from math and cancer immunology, oncology etc who all came together to work on problems in cancer treatment using mathematics. It was great fun. If you've ever been to maths in industry study group or maths in medicine study group, it was kind of like that.
I got an email from aim a few weeks back looking for proposals for workshops for next year. So I've quickly whipped one up with the help of graeme and scott and sent it off today. It's on modeling skin and related conditions. If it gets up there is a free trip to California in it for us and everyone else involved!
I got an email from aim a few weeks back looking for proposals for workshops for next year. So I've quickly whipped one up with the help of graeme and scott and sent it off today. It's on modeling skin and related conditions. If it gets up there is a free trip to California in it for us and everyone else involved!
Day 15
Bayesian beginnings
With Charisse doing a phd which involves modeling with Bayesian networks, I'm starting to read up on this sort of thing myself (so that we can still talk to each other). I've been skimming her books and also getting a few myself from the kindle store (I seem to be able to get around to reading things if they are on my iPad). The idea seems quite simple so that is good for someone like me - I don't really get statistical type modeling really. But it seems to be all about directed graphs where the connections represent the existence of some sort of probabilistic relationships between the nodes being connected.
For example, say I have A and B where A can take on values 'good upbringing' or 'bad upbringing' and B can have values 'future male stripper' and 'future chief justice' then we might have a graph of the form
A --> B
Then perhaps have probabilities like
P(B=fcj | A=gu)
P(B=fcj | A=bu)
P(B=fms | A=gu)
P(B=fms | A=bu)
And these could be calculated as an output (think bayes theorem) given certain input data like probability of good upbringing etc.
Well that's my very early limited understanding of the idea. I'm sure you folk reading this can clear me up.
Students
So this all got me thinking when someone publishes a paper in this type of stuff, what do they write about in terms of results? For example when I write a paper with say PDEs for some biological application I present graphical output for example of spatial or temporal or both, solutions. Or I present an analytical result that might uncover a key parameter relationship. So what is the equivalent for w Bayesian network paper?
Then i got to thinking about students and research training. Perhaps sometimes, because of our (researchers) familiarity with what we are doing, we forget that the actual idea behind writing a paper and presenting results is not so intuitive for everyone. I think that's something I will definitely be keeping in mind in the future with my student supervision.
With Charisse doing a phd which involves modeling with Bayesian networks, I'm starting to read up on this sort of thing myself (so that we can still talk to each other). I've been skimming her books and also getting a few myself from the kindle store (I seem to be able to get around to reading things if they are on my iPad). The idea seems quite simple so that is good for someone like me - I don't really get statistical type modeling really. But it seems to be all about directed graphs where the connections represent the existence of some sort of probabilistic relationships between the nodes being connected.
For example, say I have A and B where A can take on values 'good upbringing' or 'bad upbringing' and B can have values 'future male stripper' and 'future chief justice' then we might have a graph of the form
A --> B
Then perhaps have probabilities like
P(B=fcj | A=gu)
P(B=fcj | A=bu)
P(B=fms | A=gu)
P(B=fms | A=bu)
And these could be calculated as an output (think bayes theorem) given certain input data like probability of good upbringing etc.
Well that's my very early limited understanding of the idea. I'm sure you folk reading this can clear me up.
Students
So this all got me thinking when someone publishes a paper in this type of stuff, what do they write about in terms of results? For example when I write a paper with say PDEs for some biological application I present graphical output for example of spatial or temporal or both, solutions. Or I present an analytical result that might uncover a key parameter relationship. So what is the equivalent for w Bayesian network paper?
Then i got to thinking about students and research training. Perhaps sometimes, because of our (researchers) familiarity with what we are doing, we forget that the actual idea behind writing a paper and presenting results is not so intuitive for everyone. I think that's something I will definitely be keeping in mind in the future with my student supervision.
Tuesday, November 2, 2010
Day 14 - fuzzy
I'm half way through a paper on fuzzy decision making for locating goods distribution centres...I thought it would be interesting, but to be honest, I'm kind of bored. It's called "A multi-criteria decision making approach for location planning for urban distribution centers under uncertainty" by Awasthi et al [see here] in the latest Mathematical and Computer modelling. I'll stick it out and read the example and see if that makes it any better.
I think I have an unhealthy obsession with fuzzy logic and associated things. I'm pretty sure it has a place in formalising the way I apply rules in my CA models, but I haven't put much thought into that yet.
Note to self: put thought into that.
I think I have an unhealthy obsession with fuzzy logic and associated things. I'm pretty sure it has a place in formalising the way I apply rules in my CA models, but I haven't put much thought into that yet.
Note to self: put thought into that.
Monday, November 1, 2010
Day 13
Today I mostly did teaching related stuff due to the fact that my DEs class had their exam this morning and the PDEs class has theirs on wednesday. So lots of students visiting to ask questions. Plus I had to write the exam and solutions!
This evening though I read a paper I downloaded from a recent issue of Mathematical and Computer Modelling. Patanarapeelert et al reported on as study they carried out where they took the data generated by CA models of tumor growth ( like the ones I do ) and applied some technique to it to determine the coefficient functions for a stochastic de model of the form
dX_t = h dt + g dW.
Thereby allowing for a macroscopic model to be derived from a microscopic model.
I think I get the majority of it, but it's not all clear for me unfortunately. Plus the language was a bit scrappy but that's ok.
The problem is I'm not exactly sure I get the point of what they did. They very briefly skim over what I consider to be the most important bit in the last sentence or so. That being that they could use microscopic understanding to build a CA model, generate in silica data, build the macroscopic model and then use it to make recommendations or conclusions at the macroscopic level (eg x will happen to the tumor due to y being applied to the immune system).
Anyway I think i will ask dr Simpson if it's worth looking into it any further... Or if we (he) can do something better to the same end.
This evening though I read a paper I downloaded from a recent issue of Mathematical and Computer Modelling. Patanarapeelert et al reported on as study they carried out where they took the data generated by CA models of tumor growth ( like the ones I do ) and applied some technique to it to determine the coefficient functions for a stochastic de model of the form
dX_t = h dt + g dW.
Thereby allowing for a macroscopic model to be derived from a microscopic model.
I think I get the majority of it, but it's not all clear for me unfortunately. Plus the language was a bit scrappy but that's ok.
The problem is I'm not exactly sure I get the point of what they did. They very briefly skim over what I consider to be the most important bit in the last sentence or so. That being that they could use microscopic understanding to build a CA model, generate in silica data, build the macroscopic model and then use it to make recommendations or conclusions at the macroscopic level (eg x will happen to the tumor due to y being applied to the immune system).
Anyway I think i will ask dr Simpson if it's worth looking into it any further... Or if we (he) can do something better to the same end.
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