I tried tracking my study time and here's what I learnt

** Disclaimer: Everyone is different when it comes to studying. This is just my study data so please don't make direct comparisons and feel guilty for doing more or less. **

For reference this is Semester 2 of Year 4 of my Mathematics degree at the University of Edinburgh.

What you've all been waiting for... drumroll please...

Here's the breakdown of my study time this semester!

I've always wondered how much time I actually spend doing uni work each week. I take 60 credits of courses each semester, and supposedly 1 credit of a course equates to 10 hours of work, but is that actually true? So at the beginning of January I decided to explore how to time track my study time.

I settled on using Toggl Track, which has a phone app and a Chrome browser extension. It's completely free and has the best functionality and usability of the time trackers I've found. There is a premium version, but I've found the free version to be perfectly good.

As soon as I start studying, I just type in the course I'm studying and press the start button. Then if I go take a break of more than a few minutes, I stop the timer. It may seem rather annoying to remember and slightly over-kill, but once I got into the habit, it's not too hard to do. In the past I have tried time-tracking my whole day, but these experiments were not long-lasting. I found only tracking studying to be much more manageable and has actually had some interesting benefits:

• It helps me to keep on task and focused. I now know that when I press the little "start" icon, I need to be studying and not browsing social media on my phone. If I do want a break, I stop the timer, and this helps to keep my study time more productive than just sitting at my laptop for hours not actually doing anything. It also helps to keep a better separation between study time and leisure time in my head.

• It keeps me accountable and I feel more motivated to keep doing work and I know when I have done enough for the day. Especially during the last year with online uni, it's been hard to tell how much work is enough. Now I have a quantitative measure of how much work I have done. So at 3pm, if I see I've only done 1 hour so far, I feel more motivated to keep working. If it says I've already done 5 hours that day, I know I can relax more that afternoon.

• And of course, the end result is some amazing data to analyse 😍

Weekly study time

I generally studied around 30-35 hours per week while doing full-time uni. I usually tried to take one weekend day completely off, or at least work less at the weekend. I suppose I could condense this into 5 days of work per week with the weekend off, but I find it easier to have a more balanced life with 6 shorter days of work. Plus, I often find it hard to study more than about 6 hours in a given day.

The graph below shows the weekly hours spent studying, split up by course. I find it interesting to see that "shape" of the semester, with local maxima around W4 and W10. Flexible Learning Week (FLW) would normally be a week with much less uni work, but I had a lot of project work to get done.

It should also be noted that I was at home with my parents until the end of week 7 due to lockdown before I moved back to Edinburgh. I took a much-needed full week off in between the end of the semester and start of exam revision. Revision also eased off towards the end of exams when there wasn't much revision to be done.

Weekly study time

Courses

Advanced Methods of Applied Mathematics (AMAM - not to be confused with the karate kata "Anan" that I learnt this semester) [Sem 1, exam in Sem 2]

The course which issued a typo correction email to the typo correction email during the 4 hour exam.

• Credits: 20

• Split: 20% coursework; 80% exam

• Hours: 54h revision + exam (~80-100h in Sem 1)

The teaching for this course actually took place only in Semester 1, but the exam was in May. Therefore the only data I have for this course are the hours I spent on revision, however I estimate that I spent around 8-10 hours on it per week last semester.

I actually really enjoyed this course. Not the most thrilling course but covered lots of useful content. It was very much a methods and no proofs course. So in that vein it felt like taking a very advanced version of school maths. Although I found the lecturer rather patronising, his lectures were pretty good. However, he was also the one who put me off going to in-person tutorials during "hybrid teaching"; the online tutorials were much better with a nice PhD student as my tutor (who was also very responsive at answering questions on Piazza all year).

8.5/10 - pretty pleasant overall.

Data Assimilation (DA - no, not Dumbledore's Army)

"Worth doing once"; "You can convince yourself that..." [no... i cant]

• Credits: 10

• Split: 20% coursework; 80% exam

• Hours: 86h [55h during semester, 30h revision + exam]

My least favourite course in my whole degree I reckon. I'm not sure advanced probability is my thing. The applications of data assimilation has lots of potential to be interesting, but the course was very poorly organised (lectures uploaded 2 weeks late??) and I never really warmed to the course organiser. I'm glad I had at least one friend on this course - not a subject I would want to study alone. I kept waiting for the moment in the semester when everything begins to click into place and I appreciate the content a bit more, but it never really came. Maybe it's good that I suffered through and persevered. It's probably the first time in my life I've felt pretty overwhelmed by all the maths and really had no clue what is going on. Although by the end I finally understood what all the weird gothic symbols meant, I would probably not heed the lecturer's advice and not take this course at least once.

3/10 - only do it with a friend and for the binary marking of the assignments.

Linear Programming, Modelling and Solution (LPMS)

"You don't even have to sit the exam." 2 hours later: "You'll have to sit the exam."{Julian incorrectly thinking the no detriment was still in place this year.}

• Credits: 10

• Split: 50% coursework; 50% exam

• Hours: 75h [55h during semester, 20h revision + exam]

The hours don't especially match the difficulty as this was a fairly straight-forward course. The hours are slightly conflated by the group case study project worth 30%, which I spent 27 hours on. The whole course felt rather chaotic, with the marking slightly arbitrary. At least the course organiser answered Piazza questions any time of day (even 10pm on a Saturday night).

6.5/10 - Some interesting stuff but a slightly chaotic course

Mathematics Education (Maths Ed) [Full year]

"A behaviourist might say..."

• Credits: 20

• Split: 100% coursework

• Hours: 50h in Sem 1, (~80h in Sem 2)

This was a very different course to all the others I've taken, where we learnt all about different theories of the teaching and learning of Mathematics. But weekly readings and essays??? There was a risk I would end up regretting taking this course; however, I'm very glad I did take it! First time in my life I didn't mind having to write essays on the subject! It was extremely well organised, I learnt some very interesting things, and I had some very interesting discussions and debates on video calls with people in my year I had never met before. The course was definitely more Semester 1 heavy, and this is reflected in the number of hours. I'm very glad we got to pick our teaching project groups, as that is something that can always backfire and go very badly. However, I had an amazing little group and we worked together extremely well with tight deadlines, even when we had to write a group essay (?!) virtually. A highlight was making this video with my group, for which I taught myself how to edit videos (with the free software Lightworks) beyond the basic slideshow with music.

9/10 - Would take again.

Mathematics in Action B (MiAB)

"I'll just wait for the little message to pop up to confirm that we're recording..." {How every single live lecture on Teams started}

• Credits: 10

• Split: 100% coursework

• Hours: 58h

I don't think I actually realised until Week 1 that there was no exam for this course. Instead, it was split up between 5 big assignments, with each one taking about 8-10 hours.

The topics rotate each year and I was initially a bit disappointed to see that the topic chosen this semester was electromagnetic theory of materials, as I'd read they sometimes have more exciting ones like epidemics or data science. Electromagnetism sounds pretty dull, and it was. However, I did learn and develop a few useful skills with all the coding and report writing. Plus I got to study the famous Maxwell equations printed on the floor of JCMB (the maths building at Edinburgh Uni)! No exam also meant no revision, so the total time spent on this course was on the lower side compared to some other 10 credit courses.

7/10 - I think I'll take Maths in Action A next year with the hopes of getting a more interesting topic.

Numerical Partial Differential Equations (NPDEs)

"So just to summarise..."

• Credits: 10

• Split: 20% coursework; 80% exam

• Hours: 49h [31h during semester, 18h revision + exam]

This course was meant to be level 11, but this was probably my easiest course this semester and the least time-intensive. I still don't know what the catch is. Admittedly, my project was on a similar topic, so perhaps that helped to master the concepts a lot quicker. The course organiser was extremely well organised (take notes DA lecturer), and even provided us with a summary sheet so I didn't have to make one myself during revision.

Thank goodness for 1.5x speed on lecture videos, as the lecturer would keep recapping things from the previous video of the previous part of lecture, even though there were 6 separate videos every week.

I didn't have any friends taking this course so I mostly took it this year to help with my project. But could have waited to take it next year when most of my maths friends will have left anyway.

8.5/10 - Easy, slightly dry and repetitive, but useful material.

Project

• Credits: 20

• Split: 100% coursework

• Hours: 175h in Sem 2 (~60-70h in Sem 1)

You can read more in detail all about my project here. I spent a lot more time than I think you're supposed to spend on this project in Semester 2, and it's probably the first time I've actually exceeded the golden "1 credit = 10 hours" rule. Motivating project work was a bit harder in Semester 1 and the start of Semester 2, but I got rather into it later on in the semester (you can see I spent around half of my study time on it in the graph above). Perhaps being in a group project would have meant less time required for writing. However, in many ways doing an individual project was far more time-efficient, without the hours and hours of inefficient Teams calls with a group. I had around 45 mins - 1.5 hours of supervision meetings per week, (although once the supervision went on for 2.5 hours...), so probably around 20 hours in total. This is probably the course that has taught me the most skills and lessons this year.

9/10 - A good experience with an interesting topic and very nice supervisor (although there were definitely points when I was not enjoying it as much). Glad I was the only person in my group.

What have we learnt?

• I like tracking things. (I haven't even shown you my manually logged sleep data since Oct 2018 yet!)

• The difficulty and time investment for Level 11 courses varies greatly (compare DA to MiAB and NPDEs...).

• No exam usually means less time (project is an exception).

I think I'm definitely going to keep using this time tracker for work and study.

Anyway, time to celebrate and relax this summer before I begin my research project at Imperial in July and my final year of MMath at Edinburgh in September 😮.