Radical Imperfection in Self Tracking

  • Location:
    Veteranenstr. 21, Berlin 10119

School of Machines, Making & Make-Believe presents a five-week live online program exploring difficult data and the quantified self.

How can we create sustainable, transformative, systematic self-reflection practices?

/ Five-week Live* Online class begins 3. November, ends 1. December

/ Every Tuesdays, 7pm-9pm, CEST

/ Small class of participants

/ €176.68 – €261.79

/ Event Link: https://www.eventbrite.com/e/radical-imperfection-in-self-tracking-tickets-124704128405?aff=erelpanelorg

Course Description

Do you know how many steps you walked yesterday? How long you looked at your phone last week? How many books you read last year? What information do you collect about yourself, in order to understand yourself better - and does it feel effective and sustainable? Voluntary data tracking, although it has potential for exploitation and betrayal, can be used to deepen the understand of oneself in nearly every aspect of life [1]. The increasingly wide range of hardware and software allows this pervasive data to become “a ‘prosthetic of feeling,’ something to help us sense our bodies or the world around us” [2]. It is not just the sensors and processors; the manner of thinking that comes with self-tracking can also be applied to qualitative, “analog” systematic self-reflection, as it is in [3].

In March 2020, as one way of expressing some degree of control in an uncontrollable situation, many people experimented with new habits and with new ways of monitoring themselves. At the beginning of every new year, some version of this invigorated self-reflection and self-improvement wave swells, and then disappears in a few months. In practice, even with great motivation and commitment, is very difficult to establish sustainable, transformative practices. When it comes to systematic self-reflection, or self-tracking, one of the challenges is difficult data: missing, unreliable or changing, or uncomfortable. Facing this challenge together is the aim of this course.

This course is a practice-based investigation of difficult self-tracking data: we accept that data from any one source is imperfect, and work instead on combining data sources and constructing personal, deep self-observation practices.Each class session will include review of key concepts from current socio-technical systems research on the subject, and include sharing of personal experiences with weekly self-tracking assignments. The middle three weeks will focus on one of three subjects: sleep, movement, and mood. Over the five weeks, every participant can work on a larger project, focusing on tracking one particular aspect of their lives through a variety of approaches.

Lecture materials will draw from current scientific research on self-tracking approaches in human-computer interaction (HCI) as well as from these books:

Deborah Lupton, “Quantified Self” (podcast episode interview, where the author summarizes the book: https://newbooksnetwork.com/deborah-lupton-the-quantified-self-polity-2016/)

Gina Neff and Dawn Nafus, “Self-Tracking”

Giorgia Lupi and Stefanie Posavec, “Observe, Collect, Draw!” (you can search for #deardata on Instagram to see how people engage with this practice-based book)


Week one Introductions to each other and to difficult data

In this first class, there will be introductions of who we all are but also discussions regarding expectations and what you hope to gain from the class. In the second half of the class, we will introduce the different kinds of difficult data and discuss strategies on working with it. Topics around difficult data include when data is missing or elusive; when it is qualitative or changing concept that refuses to be usefully quantified; when it it coercive or uncomfortable. We will also share our experiences and motivations in self-tracking, setting the tone for the rest of the course.

Week two. Sleep

In week two we will discuss sleep tracking and passive vs active data collection, as well as the variety of motivations and interventions connected to sleep tracking.

Week three: Movement

In week three we will build on the model of active/passive data collection and consider how movement tracking relates to habit formation.

Week four: Mood

In week four we will use mood and emotion to dive deeper into the quantification of qualitative experience: challenges, motivations, and strategies.

Week five: Final Project Showcase

In this final week each participant will have time to present their project, focusing on tracking one particular aspect of their lives through a variety of approaches.

Who is this course for?

This class is for anyone tracking personal data (whether digital or analog) or those who would like to. If you do any form of systematic self-reflection, like keeping a journal/diary, and are curious about this from a data-tracking perspective or if you simply feel curious and/or critical about any of these subjects, come join us. Enthusiastic like-minded community included. No experience necessary.

International participants welcome!

The classes are live?*

Classes are 'live' meaning that you can directly interact with the instructor as well as with the other participants from around the world. Classes will also be recorded for playback in case you are unable to attend for any reason. For specific questions, please email info[at]schoolofma.org


Kit Kuksenok

Kit Kuksenok is a visual artist with a research background in Human Computer Interaction, and experience as a code/data worker in both academia and industry. Their interest in self-tracking, both in analog and digital media, has only grown since first trying out tracking work time in a spreadsheet in 2008, as a result of watching Randy Pausch’s Last Lecture. They have used many software and hardware tools for tracking, including using their own analysis scripts; since 2019, a large portion of their self-tracking activities has been pen-and-paper, and all strongly influenced by yoga philosophy.

This course draws on a long-term personal practice of self-tracking, not only technical knowledge and experience. While working on their PhD at the University of Washington in Seattle, they co-taught a graduate course on information visualization, and another course on data analysis using a mix of quantitative and qualitative methods. Through working with natural-language applications in the tech industry after finishing graduate school in 2016, they have a wide range of experience analysing complex structured and text data.