After putting my first post up here on Substack, I was glad to hear from people all over the world who have been involved with Quantified Self projects of various kinds. People who were graduate students when we did our first Quantified Self Public Health Symposium at UC San Diego are now teachers and researchers whose course assignments sometimes involve inviting students to use their own self-collected data. Mostly this is happening in CS, Data Science, medicine and allied health, and community programs.
What struck me from talking with people and following links to their work was how effective the Quantified Self Show & Tell format is as a way of sharing knowledge about highly personal empirical practice. People learn best from examples, and although the theory of personal science has advanced, students encountering this form of reasoning about their data for the first time still need to see how it’s done to learn how it’s done.
This is typical of all kinds of science education. We don’t get a sense of research practice until we’ve worked in a lab and seen it firsthand. Something I remember, hopefully not incorrectly, from the chemist and philosopher Michael Polanyi, is that no research program was ever effectively launched without direct, in-person participation from scientists who were already accomplished in the relevant field. Science, he argued, has always been a skill passed on hand to hand.
So, inspired by readers’ encouragement, I decided to spontaneously record a podcast with three of my colleagues who have many years of practice teaching personal science both in a university and in a community setting. I asked them about what they’d learned from working with their students, about the biggest barriers they encountered, about what the students gained, and what surprised them. We even got into some of the details of managing a class that had such high demands on students’ creativity, what kind of homework they gave, and how they graded final projects.
The half-hour episode with our conversation is here. I hope you’ll be patient with a few rough spots in the levels; we literally decided to go for it in a short Slack message and recorded the episode during our regular research meeting the next day.
My guests were Sara Riggare, Thomas Blomseth Christiansen, and Martijn de Groot.
A few highlights
Sara, on what she’s actually giving students and patients: “My role is to give them permission. For the community people, permission to think for themselves; in the university course, permission to think of themselves.”
Martijn, on the “essence” of personal science: “You make structured observations, and the data that represents those observations shows something to yourself that is valuable for you — not necessarily for the rest of the world.”
Thomas, walking through how a vague interest becomes a manageable project: “My area of interest could be my pollen allergies. Within that area, a question could be, ‘What are the dynamics of my sneezes during pollen season?’ And in there, there’s a phenomenon, which is sneezes.”
Nine Tips for Teaching Personal Science
It helps to start with a few concrete examples rather than theory. The “what did I do, how did I do it, what did I learn” Show & Tell lets students quickly tune in the flavor of a successful self-tracking project.
Explicitly give students permission to pose “small” questions. School trains students to think only big generalizable questions are worth empirical investigation, so they have to get over this barrier.
Iteratively workshop each student’s question before tracking starts. Many of them will have to revisit and “de-scope” their question, and they learn a lot from this process. (Though it can be frustrating.)
Encourage them to do a short tracking period, even just a few days. This encourages them to do the full cycle, and not get stuck in endless self-tracking.
Use freshly collected data rather than existing historical or wearable data. Passive historical data is too “prepackaged” and lets students skip the hard, formative work of defining an area, question, and trackable phenomenon.
Cap written reports at a page to a page and a half. A short report is enough to show whether a student has grasped the method, and it makes it possible to give them better feedback.
Build in a peer Show & Tell where students explain their project out loud. The act of explaining, not just doing the project, is what makes the experience “become real.” The explaining part is often when they learn the most.
Address epistemological anxieties. Personal science requires a different approach than biomedicine, public health, sociology, and other group oriented research fields. For health-trained students in particular, surfacing their randomized-controlled-trial assumptions explicitly helps them stop expecting causal, generalizable conclusions from a single-subject project.
Be permissive about the tools students use. There are many ways to turn data into a learning artifact, including pen and paper and disclosed AI assistance. Lowering the technical bar keeps the focus on the personal-science method rather than on data-tooling skill.
Links:
