I am a chronicler. I have a FitBit that records my overall health, which includes my steps, calories burned, heart rate, and sleep patterns of each day. I log my weight and everything I eat on MyFitnessPal. I also record my purchases and employment opportunities on a spreadsheet. It makes sense for such a recorder to also log grades received and the times spent on activities, studying, planning, job hunting, and work.

When I was a student at UC Berkeley, I recorded the exact percentages of the grades I received in each of my classes from each assignment as much as possible. I also began a calendar diary in December 2012, inspired by James Maa’s Productivity Hacking Guide. My calendars and event descriptions detailed when I went to sleep, took a shower, ate, or worked. I turned it into a way I could boost memory and track overall productivity. Now, I can go back to any given day and sum up all of the feelings I had that day; even though I have a poor declarative memory (episodic). So far, I’ve logged over 10,000 instances of time data, equating to almost four years of history. I don’t plan on stopping.

As a result of the data I’ve accumulated over my whole college career, I created a Productivity Project that touched upon some points I was interested in, which were:

You can view the longer full analysis and background there, but I’ll share the summary of my findings here.

DISCLAIMER: My findings here worked for me, but they may not work for you. Proceed with caution.

Findings

Time: How does a student’s grades and academic time commitment change over time?

The amount of time I spent productively throughout the years looked like a bell curve: I peaked my junior year in Fall 2014, and the lowest productive times were my very first and last semester. As I continued my beginning years, I became more motivated to work, but as graduation loomed closer, I spent less time being productive. My GPA also continued to fall consistently until my final semester at Cal.

The emphasis I place in certain productive categories depended highly on what period I was in my life. My first semester (Fall 2012) was all about figuring out how Berkeley classes and grades worked, and spent most of my time studying. My first semester junior year (Fall 2014) was all about how to spend time for the most good, and spent most of my time on activities. My final semester (Spring 2016) was all about how to not fail my classes so I could graduate and get a full-time job, and spent much of my time job hunting and studying. My GPA was the highest my first and last semester most likely because that’s when I was paying attention to doing well without underestimating anything.

Senioritis happened to me. Not in academics, studying, or preparing for jobs, but through my organic decrease in activities. While I reasoned this to weaning out of organizations to make way for new members, it also represented my lack of motivation and the increased stress with graduation requirements and job hunting.

I went through college in periods, and the category I spent the most time in showcased where I was in my life. Gradually, I grew more excited to leave this university, and thus my productive time slowly decreased when that thought loomed overhead.

Academic Correlation: Are grades received correlated with time spent, units, test scores, and class sizes?

I wanted to determine once and for all whether or not these factors were related to each other in some way. Did more studying result in a higher score? Does going to class or office hours result in higher grades? What about units or class sizes? I tackle each piece in my project. The following are answers to big questions I was interested in.

Non-Academic Correlation: Are grades received correlated with fun, sleep, and activities?

Next, I tackled factors that were not related to the class itself, but factors that students could choose throughout the semester, such as how much they wanted to have fun, sleep, participate in extracurricular activities, plan, and job hunt. The following are answers to big questions I was interested in.

Note: I also recorded categories such as exercise, cooking, eating, and moving, but exercise was such an embarrassing small category, and everything after was clumped into one, and thus all were ignored in this analysis.

Grade Deflation: Does it exist?

At UC Berkeley in particular, grade deflation is a major worry. After seeing the Daily Cal‘s data on the higher average grades over other schools, it appears that Berkeley frequently gives lower grades than other schools across the nation. I sought to study this by comparing my grades received to the percentages that I actually received at the end of the semester, and compared predicted raw scores to that grade. For example, I assumed that a 3.0 (B)’s percentage range would be between 80% and 85.9%, while a 3.7 (A-) would be between 90% and 91.9%.

After plotting the data, the majority of my grades received were frequently higher than the percentages awarded, not lower. This suggests that professors reward higher grades depending on the averages of the class, which seems to suggest that there is no grade deflation. Check out the graph on my project’s “deflation” section.

What helped me stay on track in terms of where I was in the class was to record my grades and averages of the class, while keeping in mind my position near the mean. If the average was a B+ according to ScheduleBuilder and I was close, then I figured I’d get a B+, even if my raw percentage was a 70%. As a result, I was rarely surprised at the grade that I received.

Something to keep in mind is that we’ve also discovered that class size and grade have a moderate negative linear correlation. In the grade deflation article, UC Berkeley is compared with the likes of Harvard and Stanford, both of which boast low faculty to student ratios. Perhaps the reason for their higher grades is because they have consistently lower class sizes than UC Berkeley’s.

There are a lot of different factors at play when it comes to the final grades: how much time you sleep, have fun, study, go to class, test scores, etc. However, it is important to note that most likely it will not just be one of these factors that make or break your grade. They’re all important and work together to present your results at the end.

Here are some recommendations I’ve found from this analysis, and perhaps you’d find them helpful:

What do you think about this data and the conclusions I’ve presented here? I encourage all comments and look forward to an open discussion. Feel free to contact me if you’re interested in playing around with the data more deeply, as I am not an experienced data analyst. If you have similar data, I would love to compare and contrast with you, and would highly recommend everyone to try their hand at recording. You never know what would come of it!

Edits and Updates