Productivity and Performance in College
At the QS Bay Area Meetup #40, Tiffany Qi shares four years of data that shows how she spent her time at UC Berkeley. She asks important questions about productivity, performance and searches for any correlation between time spent and its impact on her success at UCB. She also comes to some conclusions about what is a successful student career.
CalenTools | Google Calendar
My name is Tiffany. I’m here to talk a bit about productivity and performance in college, so I actually recently graduated from UC Berkeley in spring 2016, Go Bears.
And during that time I actually did my very first Quantified Self habit, which was to categories my time. So essentially, all my four years except the beginning couple of months of 2012, I did a calendaring sort of system where I would say, these are particular categories of my life. So for example, going to class, studying, walking around, or eating or something like that and then I’d say from this time to this time, this is my category, this is what I did that particular time and I just did that for my whole four years.
And so, I did this primarily because I really want to remember what I did in the past. My memory is really terrible when it comes to different events, so I thought it would be really cool that if I could remember everything that happened. In addition I could also log what I did in the future so that it would be easier for me to remember certain things.
And so that led into a different couple of Quantified Self habits that I’ve retained over the years, for example, how much I spend per week, what my productivity is like per week. I was also able to quantify my goals, since I was able to distil my goals down to being healthy, being skilled, and being understanding and I’ve been able to do that through logging how much time I spend, given a couple of key words I can talk about that later.
I also have a bunch of other fitness things like Fitbit, MyFitnessPal to track calories burned, heart rate and stuff like that.
And so while I was in college, I was really interested in a couple of things. One, how does a student’s grade and academic time commitment change over time. How are grades correlated with the time you spend on homework for example and your actual test scores, and three, whether grades were correlated at all with fun and activities?
And so the process, as I mentioned before, I would calendar, I also made a script that calculated how much time per week for a particular category was. I used to do it by hand and that took an hour per week and was like, this takes too long. Let me like make something to make it a lot faster. And at the same time I would calculate I would receive on a particular assignment or buck it like participation, test, project, stuff like that and put all that information into one spreadsheet. So in one particular class I would know exactly how much time I spent studying, what the grade was, the time spent going to classes, and I was also able to determine how much time I spent on fun, sleep, activities, for that particular semester.
And so this is what it looked like over time.
So the bar graphs represent the categories per semester. At Berkley we have a semester system so eight bar graphs. The faint blue line represents my semester GPA, and the red line represents my cumulative GPA, so essentially my cumulative GPA decreased over time as a result of the peaks and valleys, my semester GPA. Other couple of other things to note was that I planned significantly more in the beginning of my undergrad career and I spent a lot more time job hunting at the end of my career.
My homework and class was relatively steady throughout my semester except for the fall in 2014 where I spent like 700 hours on activities and jobs and organizations etc. here’s a graph of the grade I received on the time I spent on homework.
So here, the coefficient determination is like .004, which is very very little. And if you remove the two outliers at the top then it becomes .104. So there really isn’t a correlation here.
In terms of studying for a test, so that UC Berkley we have something called dead week, which is a period of time where students dedicate to studying or not studying at all for the week after, which is when people actually take their finals. So I was able to isolate exactly how much time I spent studying for a test for a particular class. And here again, the coefficient determination is like .01 something, and essentially this graph coupled with the previous graph shows that really the time you spent studying for a class isn’t necessarily linear correlated with the actual outcome.
And I think of this in two different ways. The first is because there might be a class that I actually do really well in, and as a result I don’t need to spend that much time studying for it. On the other hand there are classes that I don’t really understand the material at all, and I have to spend a lot more time studying for that class. And because of those two different factors conflict, there appears not to be a linear correlation but there may be other factors that play here.
In terms of form, this is how I spent my time essentially. Fun in this particular case, constitutes time spent on hobbies, hanging out with friends, being with my significant other, watching TV and playing games and what not. And what’s interesting here is the last three graphs was the time I spent with my significant other, as I got a boyfriend during that time. And at UC Berkeley, we have a saying, where having a boyfriend is like having three in a class on top of your original workload. So I was interested in seeing if that had some correlation as well.
And so this graph you see here actually has a point coefficient determination is .0052, and if you move the three points where I have my relationship it actually becomes .3352, so it increased by a lot.
In regards to activities, activities is classified as time I spent with a job, on a student organization, so for example I spent a lot of time like building up a student organization at Berkeley, which accounts for the ridiculous amount of time that I spent in the fall of 2014.
The coefficient determination in this particular graph is .249, and actually if you remove the fall of 2014 altogether, the coefficient determination increases by a lot, .9132, which is an extremely positive linear correlation.
So a couple of takeaway’s here is it’s really really cool seeing all the data that I was basically able to accomplish. And there are a couple of different factors that are involved in the grading each semester. Even though I’ve simplified the model in terms of grades versus this, versus that really, it’s a lot more complicated than that. A lot of different factors can be involved in the grade such as difficulty, the Prof I’m with, the time I spent over all like sleeping, activities or whatever, and the semester is the same sort of thing.
And correlation doesn’t necessarily imply causation. I’m not saying that one thing definitely cause this other thing. I just thought it was pretty cool that these sorts of patterns came up.
In general, eight semesters as I said before, eight semesters isn’t enough to say that it’s definitely correlated. But in general, I noticed that the time is spent on activities and fun might possibly relate to grades. In addition, the time spent on homework and tests because of the dual nature of ability to do well in one class versus not can kind of go into play there. And other things I just thought about, would I do anything differently after seeing this data, I don’t really think so. I think what’s fascinating is the fact that activities was so correlated with my grade outcome, but like you saw on fall 2014, there are definitely going to be outliers there, and those activities actually shipped my time a lot at UC Berkeley, so I don’t think I would have spent my time any differently in that way.
So this is my contact information. I actually have a lot more analysis, like for example I looked into my work load, class size etc. I just wanted to show the most important things. So feel free if you want to contact me if you want to look at my data. I’m not a data scientist and maybe I will one day, but not now. If you want to do any complex or anything, thank you very much.