Playing with data: What university has the most fun?

Just today, I came across a Maclean’s article from March about the self-reported workload of university students in Canada.  Aside from the fact that it’s hard to work with their numbers without any measure of variance (good call Warren!), it’s quite timely with all of the student wellness and mental health ideas going around.  I felt like poking around with some data, so I asked 3 questions and made some pictures.

1. In which schools are students doing the most partying and studying?
(I like to think about this using the FTW ratio*) 

BarGraphFunToWork

This is sorted from most fun to least fun (at least by the numbers!) For my own interest, I’ve coloured in the schools that I’ve either attended or taught at.  At Toronto I did both.

Fun to work ratio – things I notice:
  • Nobody gets better than a 1:1 ratio of partying to work time
  • If you love to party but still want to go to university, hopefully you speak French or want to live in Halifax! (both of which are awesome!)
  • Poor U of T… I don’t know if I agree with this, in my own experience.  I had a lot of fun there and I’m pretty sure the first couple years of workload during grad school didn’t match up.  But I bet they didn’t survey grad students 🙂
  • I wonder how this kind of data will look in 10 years when we have even more active-learning classes?
  • Of course this data isn’t comprehensive compared to my normal FTW calculations: there are lots more ways to work, and lots more ways to have fun… and also for some nerdy types who make blog posts like this on a Friday after work, the two activities aren’t mutually exclusive.
2. Are students working the same amount at different types of schools?

BarGraph

The type of institution is from the annual Macleans rankings.

Average workloads by school type – things I notice:
  • Looks like everybody parties the same.  “Work hard and play hard” is not really shining through in this data!  Though you certainly can party really intensely, and study inefficiently.  
    • (and again the point from earlier – asking students “how much do you party” leaves out a lot of other personal time, and selects for a subset of students who consider themselves “partiers”)
  • I’d be interested to see the differences in work and play time between 1st-year and 4th-year students.
  • Students at Medical/Doctoral schools do about 3-4 more hours each week than the others.  I’d be interested to see the proportions of students at these schools that take part-time courses, and are working outside of school.  About half of Canadian university students also have a job, but this isn’t split by schools or by program within that school.
3. Does student workload correlate with university rankings?

Scatter1Scatter2

Everyone has their own metric for ranking schools, but to keep everything tidy I used the same source of data.  Macleans ranks schools every year, with their best ranked school within an institutional type given #1.  The highlighted points are again just for my own interest – where I’ve taught (yellow) and where I’ve attended (green).

Student workloads by school type – things I notice:
  • For a top-5 good school that really knows how to party, go to SFX!  (That’s the high point on the partying graph.)
  • Student study time accounts for about 20% of the variation in school rankings. So the higher ranked schools have harder working students, but also a lot of other things go into these rankings.
  • Hurrah for SFU!  Ranked #1 comprehensive university in Canada!  Study and party time are both pretty low, relatively speaking. I’ll have to keep these student expectations in mind in comparison to my prior teaching positions.
Anything else you see in this data?

 

 

* The FTW (fun-to-work) ratio: an authentic metric that I personally use to make decisions about doing things in my real life… just ask Dr. Alex Beristain.  In this case it’s the number of hours of partying divided by number of hours of studying.  But, it’s a very flexible value and so other times it has very different units.  For example, it’s a very useful calculation to do when deciding whether to go for a run or whether to eat nachos.

 

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