The Secret Language of Comics: Visual Thinking and Writing

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How Great is my Life Going?

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Days with Score Above 15: Very Good Day
Days with Score Between 8-15 : Average Day
Days With Score Less than 8: Bad Day

I wanted to track the amount of times that I was performing actions that gave me a sense of comfort. These actions were to be indicative of how well the course of my week was going. Every time I would have a low count day I noticed that it was correlated to events that were making me stressed out and/or anxious. I noticed right away that the more fun or less stressed out I was, the “happier” I perceived my day to be going. I also noticed that although I wasn’t having any problems on Monday I had a low score simply for the fact that the “Monday” energy was hitting me. My “weekend” days Friday- Sunday were notably higher because I knew that I wouldn’t have to be concerned with quizzes, tests, or work.

If I were to continue this project I would have looked to make sure that even if I had personal issues during the week, I would not let them impact my mood for the day. I found this tool to be valuable for me because generally I think that most of my days are pretty terrible but being able to quantify my day with numbers helped me see that my days weren’t as bleak as I had originally anticipated. Being able to see my life as a set of bars on a graph helped me to see that I am capable of controlling how to navigate my issues, and not let them have as big of an impact on my life or of those around me.

Assignment Link: https://eng181f19.davidmorgen.org/assignments/sketches/sketch-9-data-viz-from-everyday-life/

The Happier, The Better?

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Is it always true that better moods can lead to relatively higher productivity? My data says “no!” 

For the past two weeks, I’ve been tracking the correlation between my mood and the level of my productivity. In order to be more precise on my data, I decided to focus only on my productivity when studying, which includes my time spent doing homework, group projects, class registration, advising, and all things that contribute to my overall academic progress. I chose to record the total time I spent studying, the actual time I studied, my time of highly focusing on my work, and the time I didn’t study (including looking at my phone, chatting with my friends, eating snacks, etc.) within the total studying time range. Tracking my mood was, however, comparatively harder because it is a more feeling-based rating rather than solid data. Therefore, I chose to give a numerical measurement to the abstract feeling by dividing my mood into different levels:

  • Level 1: Negative (e.g. upset, angry)
  • Level 5-6: Neutral (e.g. calm)
  • Level 10: Positive (e.g. happy, excited)

I then rated my mood at the beginning of every one of my tasks, and I averaged the ratings by the end of each day as my general mood when studying. 

When I first started this project, I expected my productivity to be the highest when I was in the best mood. However, looking at my final graph, I surprisingly found that when I was particularly happy and excited, my productivity turned out to be low. This result can be observed through my data for Friday when I was so thrilled about the weekend that I ended up spending half of my time chatting with my friends when doing homework. In contrast, my productivity was the highest when my mood level was around 6-7, which was a neutral and slightly good mood. From the graph, we can see that high productivity is achieved when the red bar (actual time spent studying) is significantly longer than the yellow bar (time not studying), and this only occurs when my mood was neutral (6-7). Other than that, I also found that being in a neutral mood helped increase my concentration. It is important to note that here I’m comparing the percentages of my time of highly focused (“time of highly focused” divided by “total time spent studying”) between different days, rather than comparing the actual hours of highly focusing time because the total studying time varies from day to day.

Presenting my data in a visual graph was time-consuming. I used to record my daily data in a hand-drawn spreadsheet and was about to do so for my final version of the assignment. However, since I have never tried using digital software to create charts before, I decided to give it a shot. After hours of struggling, I managed to create a visual chart on Infogram.com. The visual chart is certainly a valuable tool since it visualizes my study habit and relates it directly to the level of my mood. I’m definitely inspired by the data to calm myself down before I dive into my work in order to achieve the highest of my productivity, and I should probably avoid studying when I’m thrilled.

What am I busy with?

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sk9

I developed this habit of listing pending matters at the beginning of this semester after a terrible week haunted by time conflicts. To avoid over-scheduling myself as well as procrastination, I write down my everyday work on a notebook for better time management. This sketch assignment makes me realize that the notebook can also be used to trace back my daily life and identify potential patterns. Although I do not put down everything in my life, items on the notebook are usually things that occupy most of my time when I am not in class. This graph represents data from Sep. 23rd to Nov. 10th.

My Sunday to Monday is mostly occupied by academics and less time is spent studying since Wednesday because I tend to finish assignments ahead of time so that I can enjoy the rest of my free time without thinking about dues. Most of my extracurricula are scheduled on Thursday night and Friday because I do not have classes on Friday. The drop between Friday and Saturday is easy to understand – Friday nights never end on Friday.

I have thought about showing the percentage of each category, but the different amounts of free time I have make it more reasonable to record the actual numbers. For instance, my Saturday always begins in the afternoon, and I have to wake up early for classes on Tuesday and Thursday. If I were to continue this record to assess my schedule, I would use total time spent in each category instead of the number of items, since time spent on each item may range from 30 minutes to 12 hours. Except for the fact that staying up late is unhealthy, I do not see the necessity to significantly change my typical weekly schedule.

 

Assignment Link: https://eng181f19.davidmorgen.org/assignments/sketches/sketch-9-data-viz-from-everyday-life/

A Week in Life of an Emory Scholar

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For this Sunday Sketch, I decided to look very closely at how four different factors affected my overall study habits at Emory. I also wanted to know what type of assignments should I complete to varying points during my day to ensure my time is being used effectively. Some tasks require lots of energy and focus while others are more simple and just need some minimal motivation. Therefore, these factors were my levels of Energy, Motivation, Focus, and Productivity. I ranged each element from 1-5, one being very low and five being very high. I then looked at how these levels fluctuated throughout my day and throughout my week and came to some interesting conclusions. 

This research was vital to me because I wanted to see what days and what times during the days would be the most suitable time to work on a particular assignment. For example, based on the data, I can conclude that my peak focus time is during breakfast. Therefore, going forward, I can aim to complete all my readings during the early breakfast time because I know I’ll be able to focus on them. In contrast, it’ll probably be best to achieve my most energy-intensive assignments during dinner time because that’s when my energy levels are typically the highest. I also was able to conclude from my data that the factors that impact my study habits are directly correlated to the number of classes I have on a given day. On Thursday, I have three-morning courses back to back, and they are all very long and rigorous courses. According to my data, I experience some of my lowest energy, focus, and motivation on Thursdays.

If I were to continue this research, I’d collect more data over more days to make my data sets more accurate. On of the judgments I had to make when documenting this data was figuring out what was important enough to record. A lot of factors impact study habits and figuring out which ones to look at were difficult. It was also hard to asses sometimes how I was feeling, and sometimes I felt my level was between two numbers, so I had to pick whichever one I was leaning towards. In another study, I would make the range larger (i.e., 1-10) so that I could obtain a more accurate level.

This project was a valuable tool in allowing me to look at when are the best convenient times to complete different types of tasks. I’m currently in the process of building out my schedule for next semester, and I will be sure to return to this data when scheduling my classes and to manage my workload.

DEPRESSION, DESPONDENCY, AND DEJECTION

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The past week, for me, has been pretty depressing. Unlike the other assignments, this Sunday sketch assignment took up a significant amount of time. My goal was to simplistically quantify a subjective quantity, such as depression, based on specific categories. The five categories: loneliness, study/work, talking with family, sleep, hangouts. The results were satisfying, to say the least. The categories selected had a relatively radical influence on depression. Loneliness contributes to depression, while sleep has a neutral effect (because I would usually sleep if I was depressed). Studying, talking with family, and hanging out decreased the effect of depression for me.

I decided to use time as the unit of measurement for the categories, the greater the amount of time the greater the effect that category will have on my depressive mood. After analyzing the data collected from the past week, I could conclude that I have been in a more depressive mood at the end of the week than on weekdays. I chose to visualize the five categories in the form of a timetable as it was easier for me to quantify depression on the final scale. The final scale was a simple bar graph showcasing depression in numerical form.

The project has done a surprisingly good job. If I were to continue with this project in the future, I would’ve continued with it similarly as I did the past week, though, I would add more variables to the equation to bring about more accurate results.

Link to the assignment is here.

Image credits: https://www.theodysseyonline.com/brief-visualization-depression

Data Viz

Student Posts

When I started this assignment a week ago I had a question: Does the amount of time between eating and engaging in academic work effect my productivity? More specifically, should I be making more of an effort to eat before my first class?

Throughout the span of a week I kept track of the time and my perceived productivity and alertness. Some days I wouldn’t eat until a few hours into my academic work. In order to show this I entered the times in negatively. At the end I found that the more time there was between my first bite and the start of my academic day the more productive and alert I felt.

Though it will be difficult to wake up in the morning earlier in order to have time for a bite, I can see that it will be beneficial. And even if I oversleep I will try to have a banana or some sort of quick snack in my room.

What Should I Do Throughout My Day to Play Better Chess?

Student Posts

I tried to figure out what things throughout my day impacted how I played chess. I always play about an hour of chess everyday, so I decided to record how many wins, losses and draws I had and the types of games that I played. Sometimes I would win or lose based on an outplay but other times it would end because a player ran out of time. I also recorded how I spent my day and how happy I was to see if there was any correlation.

In terms of results, I found out that I won more games and better games (outplay oriented instead of time oriented) when I studied more, had more classes and had more physical activity. The physical activity makes sense but the studying and classes surprised me. I thought I would be tired of thinking from classes and slack off in chess, but that didn’t seem to be the case. I am also surprised that sleep and mood did not play a huge role for my win loss ratio. I was also surprised that my puzzle rush score (a chess related warm up game that I played before my games) did not correlate with any of the statistics as well.

I was able to answer the question that I asked. What I do throughout my day definitely impacts how I play chess. Rating my day on a scale of 1-10 was super difficult because my mood fluctuates heavily throughout the day and it was hard to find an unbiased point where I could logically rank it. I chose the three graphs as shown below to organize and compare my data. The first two graphs organize the data shown in the charts below. I made the first chart by category as opposed to by day because there were too many categories in play. My third graph is analysis of the data. I made it to compare day by day instead of by category. These visualizations show that I am a very logical person and love ranking things. I am always the type of person to rank just about everything in my day to day life. I think I would go about this experiment similarly; I would just add social interaction with friends. I think there might be some correlation there. In my opinion, this was a very valuable tool for self analysis!

Here is a link to the rubric!

Feeling Fine with Less Screen Time

Student Posts

Question: Does the amount of time I spend on my phone affect my general mood at the end of the day?

In order to answer this question I used my tracked my screen time usage on my phone through the tool in settings and tweaked the data it output in order to make it more informative for me. For example, the program initially classified Youtube as entertainment, but I often use it for studying by watching how-to videos, so I made sure to correct for that in my data when I recorded it every night. Then, right before bed I recorded my overall mood, trying to look at the day and how I felt as a whole. While I recorded short phrases in my notebook every night such as “feeling content, had a decent day,” I found it easier to convey this information in my infographic as simply a smiling, straight, or frowning face.

When analyzing the data that I collected I noticed that total screen time didn’t directly predict my general mood that day. For example, the first Friday and Saturday in the infographic had a similar total screen time, yet on Friday I felt sad and on Saturday I felt happy. On closer analysis I noted that I had spent significantly less time on social media on Saturday than I did on Friday. After noticing this pattern I looked across all my data and noted that of the five days with my lowest screen time spent on social media, I recorded feeling happy for four of those days and feeling neutral for one of them. Furthermore, of the four days with my highest time spent on social media apps, I recorded feeling sad on three of the days and neutral on one of them. This trend leads me to conclude, as one might have already suspected, that extended usage of social media leads to a general decline in mood.

I was somewhat surprised that there was not also an overall correlation between screen time and mood, but when I think more about it, it makes sense that there is not because my phone can be used for both helpful and harmful purposes. One flaw in my infographic is that while I think dividing my mood into good, bad, or neutral helped simplify my conceptual data, it also ignored the nuance of emotional health. If I were to do this again I would also track my mood in a quantitative method such as how many times I felt sad or I laughed, so that I would have more concrete data to back up my assertions.

11 Hours of Sleep is not Productive

Student Posts

I struggled to determine what and how I would measure this Sunday sketch. After a long process of internal debate, I realized I am most curious about how productive I am day to day, and whether my level of productivity is dependent on how much sleep I get or the time of day. Especially with Spring semester course signup around the corner, it is beneficial to at what time of day I am most productive. What I valued most while collecting data was waking up and counting how many hours of sleep I had gotten. It was nice to have a concrete understanding of how many hours I am getting on average. I also must admit that occasionally the prospect of having to input data on how productive I had been made me want to be a little more productive in times when I typically lack motivation.

My data collection, via notes on my iPhone, is relatively inconclusive. During the past two weeks when the data collection occurred, I was in very different places. I averaged the data between the two weeks, but in reality, I was much happier and energized in the first week and quite sick and fatigued in the second week (hence the 11 hours of sleep!). I also must admit that my definition of productivity was more of a feeling of general accomplishment than a concrete measurement based on hours of work or numbers of assignments completed. I decided to base it on a feeling because that is what I believe affects me more, how I feel about what I have done, not what I have or haven’t done. As I continued to input numbers as the days went by, I began to create something of a base sense of productivity to base my measurements off of.

Once I had all of my data, my next biggest challenge was to show my findings in a simple format. Since I have not been feeling well, I have to be realistic about my ability to focus and must play to my strengths. I am good with pen and paper and unfamiliar with web design, so I decided to stick to my notebook. I have always found bar graphs to be clear and concise and I separated my data into three parts for easy comparision. I found that I am typically more productive in the hours between noon-midnight. I also discovered that I get a similar amount of sleep each night, and when I get much more sleep than normal (11 hours), I am less productive. I wish I could continue this study throughout the year and incorporate more elements such as exercise. I feel that most of my data is relatively similar and heavily relys on my class schedule and health and therefore is not the best indicator of what truly effects my level of productivity.

How does sleep length effect my alertness?

Student Posts

As you can see (hopefully), I constrasted how much sleep I got, the quality of my sleep (how I felt on a scale from 1 to 10 each morning), and the visual of my natural face each morning.

I was pleasently suprised by the results. It seems that I felt better the more sleep I got (given that these are very rounded answers), and that, at least from my perspective, I generally look slightly more awake in the pictures with more sleep/better feeling. But that’s really subjective and it’s hard to be consistent every morning with making sure my face is in a natural, resting position.

This project was hard. At the very start when I heard that we were doing visual data I immediatly thought of comparing how awake I looked when I woke up (through pictures) versus how much I slept (recorded on the app Sleepcyle). I did not put any thought into how I was actually going to present this data. My main query was wondering if you can tell a difference in how alert you look given more or less sleep. When I brought this idea up in class David suggested I keep track of some other data point, so I decided to also record how I felt each morning on a scale from 1 to 10.

In the last couple days I have spent too much time trying to figure out how to show these three data points (one being pictures) on one visual chart/image. I first leaned towards some sort of graph with data points, because I didn’t want to deal with the pictures. So I created an area graph of how much time I spent in bed versus how much time I actually slept (all over how much sleep I got). I thought it looked super boring however, and I still wanted to find a way to include the pictures. Then I moved to a scrolling system where the further down you scrolled on the pictures the more sleep I got. And finally after I got frustrated with the plainness of that idea I settled on putting the pictures themselves on a horizontal timeline.

Even at this point I hadn’t figured out how to incorporate my third variable: subjective quality of sleep (or rather how I felt waking up). I came upon the solution quite by accident. I was dragging my photos into a photoshop file and resizing them so they could all fit on the page when I thought: “wait what if photo size indicated quality of sleep”. From this point on my frustration from this project looked like this:

here is an example of me spending too much time on this assignment

… and thus I came to what I have in that final image/graph/picture collage.

If I were to do this again I would have figured out how I was going to present the data from the start, because I did not choose very presentable data points (or rather… pictures). This didn’t really change the way I looked at my life, but rather affirmed something I could’ve guessed: I look and feel more alert when I get more sleep. But then again, another person could look at those pictures and drawn the same conclusion. It’s hard to differentiate between “very tired” and “tired” in pictures. I also would have recorded more specific sleep length data.

Alright I don’t really know how to end this. I’m tired and I’ve already spent way too long on this project. Please excuse the casual tone of voice throughout this rambling post. Peace out.

(link back to the assignment)

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