The Secret Language of Comics: Visual Thinking and Writing

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Who Has My Best Interest?

Student Posts
Visual Representation of Data

A question that always runs through my mind is the exact question I tracked for the week for this post: Is Emory treating me well? I never thought I would end up here so when I did I was kind of surprised myself so this was just a test to see if I really do belong here. I decided to test my social life, what I thought about throughout the week, and how full I felt each day from the food Emory provides to me. Only for the first two I noted if it was connected to me wanting to go home or was it a feeling or action that the school and my surroundings genuinely brought to me.

In conclusion I could say that Emory is doing its best and I am not miserable here and don’t think about home 24/7. Socially I am thriving and there are things set up throughout the calendar that I am interested in and looking forward to. The hardest thing about this for me was tracking my thoughts for the mental aspect and seeing if I was really excited, fake excited, or not excited at all and it was just a blank thought. I enjoyed this experiment thought because it has reassured my place here at Emory.

Fire in your Eyes

Student Posts

his torch

in his memory’s eye

with the rumbling beach and the rising tide and the freshening stars in front and above

was to stand for the redoubled thrust of flame and would make combustion sure

This assignment at first seemed like a daunting task because I had to make my own story out of a set of predetermined sentences; however once I decided to look past that and just allow myself to be free with my drawing and poem it turned out to be a therapeutic event in the midst of hardcore studying and testing.

Link back to this assignment: https://eng181f19.davidmorgen.org/assignments/sketches/sketch-8-human-document/

How Great is my Life Going?

Student Posts
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?

Student Posts

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?

Student Posts

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

Student Posts

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

Student Posts

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.

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