Student activity monitor!

The blog has been quiet for a while, but I've been putting that time to good use by collecting data. Some time ago I realised that this little panel on the MyCourses* page had been staring at me all along. It tells you how many people from each of your classes are logged on to the system (and you can see who they are). So I basically have a way to track student activity over time. Over the past 2+ weeks I have been recording the time and the number of online students by course every time I logged on, with at least 20-minute intervals between consecutively recorded data points.


The UCS, UFB and CCB Elections category is a dummy course that contains candidate statements, endorsements, etc. for the elections and presumably, all undergraduates are 'enrolled' in this dummy course on the system. This gives me a large enough sample to look at general patterns of student activity over a general 24 hour period on a weekday. It is actually quite interesting! It looks like peak hours of academic activity are in the mid-afternoon 2-3ish and at night. In the morning there is a steep increase from 9 to noon and there is a noticeable drop around dinnertime.

Of course, this is as much of a graph of my own activity as of general student activity (since I can only get the numbers when I log on to the system myself). I am almost never up past midnight and I generally get up between 7 and 7.30am so there is a big data hole between midnight and 7. I would love to fill in some of it, particularly the 12mn to 2? 3? 4? am part because I want to know what time the number starts falling and people go to bed. But my sleep > data on other people's sleep so I may never know. (If you are a late night-early morning worker and want to help me collect data on this you are most welcome to!)

Breaking it down by class, these are trends for physics 40 (which I take) and bio 42 - ecology (which I TA). These are standardised by the total class size (physics has slightly more than 3 times as many enrolled students as ecology). They both show similar trends of steep increase in the morning, but are much more variable for the rest of the day (though if you take the average it would be pretty much a straight line from noon to midnight). My favourite part about these graphs are the outliers :)

I think this is something worth looking at again next semester, over a longer period of time. It might also be fun to compare weekends vs. weekdays and...so many things.


*people from Singapore: MyCourses is pretty much exactly like the NUS IVLE, but with a clunkier interface.

Algae people!

I attended my second scientific conference in five weeks (also my second conference EVER) this weekend: the 50th anniversary symposium of the Northeast Algal Society at Woods Hole. As might be expected from a group that focuses on algae, this was a much smaller, intimate conference than the Benthic Ecology Meeting, and they gave everyone a full list of attendees' names, affiliations and contact information. AKA graphable data. So here we go...

I wanted to look at where people were from, i.e. which institutions sent the most algae people out to Woods Hole. This graph is something similar to a rank abundance curve, with institutions ranked by the number of attendees on the abscissa and the number of attendees on the ordinate (the terms 'abscissa' and 'ordinate' make me happy because though they are so ridiculously obscure). The institutions with the biggest contingents were: University of New Brunswick, UConn, URI, UNH and Northeastern. I was the only one from Brown :)


Here is a plot of how far people were from their home institution. It is based on point to point distance measurements in Google Earth, so it most certainly underestimates the actual distance traveled by each person (especially because Cape Cod is a funny shape).


Woods Hole is awesome and today was sunny so I was very happy. Here is a picture from near the conference building.

Minimum ages

I spent most of today making regression models to analyse nutritional data from the McDonalds menu for my statistics class. It was surprisingly fun.

Unrelatedly, here is a quick graph.


Make of it what you will.

I'm not voting in this year's election because I no longer have an address in Singapore and therefore I no longer have a constituency :(

My mileage*

I should be studying physics, but I found a great distraction: graphing the total (cumulative) distance I've travelled since the start of freshman year. This includes travel by air, road, rail and water that is from one town/city to another (so it excludes general day to day moving around, going to the grocery store etc. but includes things like Providence to Newport or Nahant).

I was quite impressed. I've done slightly over 120,000 miles, which is almost 5 times around the Earth, and about halfway to the moon. I haven't quite made the entire Canadian coastline, though...
I'm going to keep this dataset and re-graph it when I graduate. How many miles can one travel in 4 years of undergrad education? We shall see.



* Obviously I am using miles because I have lived in the US too long.

"Spring Break"

My next real break will probably occur in 2012. At least spring will get here sooner...

Temperature histogram

In commemoration of the coldest dive I have ever done in a wetsuit, this is a histogram of the water temperatures I have encountered throughout my 160+ dives. Red is for warm water dives (i.e. in a 3mm wetsuit or shorty) and blue is for cold water dives (7mm suit and hood).

At 5ºC, yesterday's dive was a full 3ºC below my previous low (~8ºC at Friday Harbor).

Graphs of a blog of graphs

It's about time this happened, because there was no way I could let these pretty graphs just sit there. Blogger records visitor stats for the blogs it hosts and graphs them. So there are graphs of this blog's visitor traffic. Fig. 1 is a graph of visitor operating systems.

Fig. 1. Visitor operating systems for this blog

Compare that with the OS composition of another more high-traffic Blogger-hosted blog with stats I can access: the TA-run Brown ecology class blog (Fig. 2). Brown is rather maccy in general, but this is a really big difference!!

Fig. 2. Visitor operating systems for Brown ecology class blog

Finally, a general comparison is in order. As I did before when comparing my class diversity over time, this is basically a H' index. I wonder what Shannon et al. would think of my using their index to quantify diversity in ridiculous things, but it's the most familiar measure of diversity to me and here it is (Fig. 3).

Fig. 3. Diversity of this blog vs. the ecology blog

Of course, my real point in posting all these graphs is this idea: if I blog the graphs of the blog and then graph the blog of the graphs of the blog etc. etc. IT WOULD GO ON FOREVER!!!

Next time: web browser diversity!


As a side note: There is obviously some non-independence going on here (e.g. repeat visitors, of which I am sure there are many many many). But this is Blogger's fault and not mine and I am not making any statistical inferences here, so I'm going to pretend all is fine.