Goal of the course

There were three goals of the course. First of all, since the course was planned as beginner-friendly, we wanted that the students learn and practice the basics of data collecting by self-tracking and also analog data visualizations through various exercises. Second, we hoped that students present their physical data visualization as a final project at the end. At last, we wanted that the whole process of the project weeks would be no stress and joyful for everyone!

Timeplan

Wed, October 5, 2022: Kick-off

• Introduction to the course
• Analog Data Visualization Input (by Klara)
• Warm-up exercises 1: Which data would you like to track by yourself? Why do we visualize something in an analog way?
• Warm-up exercises 2: Creating visual variables
• Small homework: Commute log

Thu, October 6, 2022

• Self-tracking Input (by Hyeonji)
• Warm-up exercises 3: Self-tracking on campus and visualizing

Fri, October 7, 2022

• Commute log submission and discussion
• Ideation Workshop for the main project

Weekend

Mon, October 10, 2022

• Main project idea presentation

Tue, October 11, 2022 - Thu, October 13, 2022

• Work in process

Fri, October 14, 2022

• Final Presentation

Warm-up workshop 1: Which data would you like to track by yourself? What would be the reason why we need to visualize something in an analog way?

We opened our first day of the course with an introduction presentation. We welcomed the students who were participating and had a getting-to-know time with ice breaking game. After that, we informed the students about the direction of the course and introduced our time plan.

As a next step, an input presentation Introduction to analog Data Visualization was given by Klara. Klara introduced beautiful data visualization inspirations such as Dear data by Giorgia Lupi and Stefanie Posavec, also her own projects, and additionally various visual references. At the end of the presentation, we asked students and ourselves 2 questions to answer and share our thoughts.

 
The first question:

Which data would you like to track by yourself?

To answer this question, we looked back on our daily life. These answers could look very trivial, but they are reflections of thoughts, small habits, and even lifestyles. In other words, these self-tracking data could contain meaningful insights that lead us to change our old habits or achieve a goal.

 The second question:

What would be the reason why we need to visualize something in an analog way?

Since our important condition of the course is not using any digital tool for visualizing data, we would like to find the reasons for that. We have gotten very interesting responses to this question. Analog data visualization can be a very unique and personal piece because there will be only one piece as original if you visualize it by hand. And you could think out of the box and be more creative!

Warm-up workshop 2: Creating visual variables

For the second warm-up workshop, we practiced how to create a variety of visual variables with different materials, such as watercolor, coloring pencils, markers, stickers, etc. And students were able to learn how to express different parameters and legends.

Warm-up workshop 3: Self-tracking on campus and visualizing

Before the third warm-up workshop, Hyeonji gave her input presentation about Self-Tracking: A way of exploring myself through personal records. In the presentation, she introduced the basic concept of self-tracking as personal informatics with examples.

For the first start of the self-tracking practice workshop, we decided to use Self-tracking project logs from the Quantified Self community. The log consists of 4 parts: Questioning, Observing, Reasoning, and Consolidating Insight. Following these steps, we planned what to track, and how to track data on the campus. After that, we each spent 30 minutes tracking some data on the beautiful campus. And with these data, we created the first analog data visualizations. The students were able to learn how to interpret data and how to prioritize the importance of data.

You can see the final data visualizations below:

by Yu-Hui Chen, Yevheniia Shyrchenko, Wen Hsuan Yang, Klara Pröpstl, Hyeonji Kim (in order of photos)

Homework: Commute log

At the beginning of the course, we asked the students to create a commute log as a small homework They should choose a journey during their daily life (ex. commuting from Berlin to Potsdam, riding a bicycle to a park, etc.) and collect data. It was up to them what kind of data they would collect. They had 3 days for collecting and visualising the data.

Wen Hsuan Yang visualized sounds that she heard during her commute (Photo 1). Yevheniia Shyrchenko mapped her favorite street in Berlin (Photo 2). And Yu-Hui Chen drew an illustration  that showed how many Asians she saw in the last 3 days (Photo 3).

Final project

For the final projects, the students had 5 days of data collecting(including weekends). Through the feedback session, we discussed the direction of the final projects and shared our opinions. In the last few days of the course, we spent our time mostly working on finalizing the projects. Each student finalized their main projects in very different ways with completely different self-tracked data.

Yevheniia Shyrchenko tracked her mood changes for 5 days and embroidered the data on a canvas bag (Photo 1). Yu-Hui Chen drew her data records of eating with acrylic color (Photo 2). Wen Hsuan Yang collected data on what she smelled for the last few days and also embroidered (Photo 3). (We prepared 3 canvas bags and each student got a unique bag of their own 😊!) At last, one of our tutors Klara Pröpstl created a dimensional paper piece that represented moments she felt happy during the last couple of days (Photo 4).

Wrap-up

As tutors, we were happy that we could make this class with motivated and passionate students. All students were actively involved in every step of the class and completed their projects professionally. 

From the students' side, not using any digital tool was an interesting approach to data visualization. At that time one of the students had only known about what data visualization is and none of them hadn't had any experience in visualizing data. Nevertheless, they mentioned that they enjoyed the course and also had a good chance to see themselves from a different perspective while they were self-tracking.

We hope that this documentation of the course could be helpful and inspiring for the people who are interested in data Viz. or who want to get a basic idea of self-tracking and data Viz.