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.

Air consumption

These were early attempts to calculate and compare my underwater air consumption rates from Fall 2009 to Summer 2010. I compared them by general region (New England, Moorea, Friday Harbor) and by water temperature (Cold: ≤18ºC, Warm: ≥27ºC). Outliers are circled and I traced them back to the actual logged dives, where they were generally associated with strong current or 'buddy issues'.


Of course, after I did this I found that people generally standardise air consumption for depth to give a 'surface air consumption (SAC) rate.' So I recalculated and replotted and here's my final air consumption graphed by region, complete with quartiles. You can see that it's a distribution with a long upper tail.


My dive log is a ridiculously tempting source of things to graph because I log all my dive information anyway. I'm sure some of the fancypants dive computers and software can do some of this for you, but I bet they don't have fun doing it and they they can't give you r-squares and regression coefficients!! (Not that those can tell you that much for dive data anyway)