CS 2951R: Personal Informatics Seminar

Spring 2016

Data science for data about you. Computing is expanding our ability to collect and process data about our everyday lives. This seminar covers personal informatics, the collection of data from daily activities for reflection and self-experimentation. We will cover methods for knowing more about yourself through using technology to track different types of data and how to interpret them, and run controlled experiments on yourself. We will learn about self-reflection and visualization, experimental design, time-series analysis and apply them to domains of location, sleep, activity, time spent, health and wellness. These topics will be pursued through independent reading, assignments, class discussion, and a semester-long self-tracking and experimentation project. Students should already be comfortable working programmatically with data, and preferably taken a course in: data science, machine learning, user interfaces, or probability/statistics. The seminar will have limited enrollment. Please fill out this form to apply.

Note: if you've taken CS 1300, then CS 2951R is more organic, smaller and intimate, less formal, and a bit more like an experiment in itself!

We will use Slack for sharing content and posting comments about readings, and the only written handin will be one assignment writeup (A0). Reading comments should be interesting things you noticed in the reading that you'd like us to talk about in class. The assignments will be opportunities for you to do something fun with your own data, and you will share the findings in short "show and tells" in class.

Course Time and Location

Location: 477 CIT (Lubrano)
Time: 1:00-2:20pm on Tuesdays and Thursdays

Instructor

Jeff Huang, 407 CIT, jeffremovee@cs.brown.edu
Office hours: Tuesdays 2:30-4:00pm

Schedule

Day Topic Class Discussion Assignment
Jan 28 Keeping Track Didion - On Keeping a Notebook
Sunday Times - Memories are made of disks
Feb 2 Quantified Self Wolf - The quantified self (watch the TED Talk video)
Choe - Understanding Quantified-Selfers' Practices in Collecting and Exploring Personal Data
A0 out
Feb 4 Quantified Self Watch 2 Quantified Self talk videos
Butterfield - Ethnographic Assessment of Quantified Self Meetup Groups (skim)
Feb 9 Data: Location Parecki - Everywhere I've Been: Data Portraits Powered by 3.5 years of data and 2.5 million GPS Points
Thudt - Visual Mementos: Reflecting Memories with Personal Data
A1 out
Feb 11 Data: Location Neuhaus - UrbanDiary: A Tracking Project Capturing the beat and rhythm of the city A0 discuss
Feb 16 Knowing Yourself Li - Understanding my data, myself: supporting self-reflection with ubicomp technologies A1 check
Feb 18 Knowing Yourself Neisser - Five kinds of self-knowledge
Prepare a question for Jin Young Kim (guest visitor via Skype)
A0 stage1
Feb 23 Holiday
Feb 25 Time Spent Charts from the American Time Use Survey (look through the charts)
Yao - A Day in the Life of Americans
Short tutorial by Jeff to "Learn D3 in 60 Seconds"
A1 share
A0 stage2
Mar 1 Time Spent Scollon - Experience Sampling: Promises and Pitfalls, Strengths and Weaknesses A1 share
A2 out
Mar 3 Self-Visualizations Felton - Annual Reports
Dancy - Data
Prepare a question for Nicholas Felton (guest visitor via Skype)
Mar 8 Data: Health and Wellness Bentley - Health Mashups: Presenting statistical patterns between wellbeing data and context in natural language to promote behavior change
Prepare a question for Chris Dancy (guest visitor via Skype)
A2 check
Mar 10 Self-Experiments Roberts - The unreasonable effectiveness of my self-experimentation
Augemberg - Quantified Self How-To: Designing Self-Experiments
Short tutorial by Jeff to "Learn statistical testing in 60 Seconds"
Mar 15 Self-Experiments Kratochwill - Single-Case Intervention Research Design Standards
Daskalova - A Cohort of Self-Experimenters: Lessons Learned from N=1 Personal Informatics Experiments
A0 stage3
Mar 17 Data: Motion and Activity O'Sullivan - Physical Computing (Sensing Movement chapter)
Short tutorial by Jeff to "Learn supervised learning in 60 Seconds"
Mar 22 Data: Motion and Activity Sachs - Sensor Fusion on Android Devices: A Revolution in Motion Processing A2 share
A3 out
Mar 24 Behavior Change Consolvo - Activity Sensing in the Wild: A Field Trial of UbiFit Garden A2 share
Mar 29 Holiday
Mar 31 Holiday
Apr 5 Behavior Change Fogg - Tiny Habits
Klasnja - Microrandomized Trials: An Experimental Design for Developing Just-in-Time Adaptive Interventions
A3 check
Apr 7 Data: Sleep Choe - SleepTight: Low-burden, self-monitoring technology for capturing and reflecting on sleep behaviors A3 share
Apr 12 Data: Sleep Winter - Personal Sleep Monitors: Do They Work?
Daskalova - SleepCoacher: Combining Computational and Clinician-Generated Sleep Recommendations
A3 share
A4 out
Apr 14 Data: Social Dabbish - Understanding Email Use: Predicting Action on a Message A3 share
Apr 19 Time-Series Analysis Penn State - Intervention Analysis
Brodersen - Causal Impact
Alonso - Autoregressive-moving-average (ARMA) model (optional)
Keogh - Symbolic Aggregate approXimation (SAX) Tutorial (optional)
Apr 21 Data: Social Wolfram - The Personal Analytics of My Life
WolframAlpha - Personal Analytics for Facebook
A4 check
Apr 26 Data: Social Viegas - Digital Artifacts for Remembering and Storytelling: PostHistory and Social Network Fragments A0 check
Apr 28 Models of Personal Informatics Epstein - A Lived Informatics Model of Personal Informatics A4 share
May 3 Models of Personal Informatics Li - A stage-based model of personal informatics systems A4 share
May 5 Show and Tell A0 share

Assignments

A0 "You vs You" - A hypothesis-driven self-experiment study you perform on yourself

A1 "The Road Taken" - Collecting and revisiting past places you have been and routes you have taken

A2 "My Life in Pictures" - Make visuals that allow you to compare your time spent with a larger population

A3 "From Motions to Actions" - An exercise of classifying raw motion sensor data into activities

A4 "Unrequited Mail" - Make an email assistant bot to let senders know when to expect a response

Important things to know

Collaboration policy: if you use something (code, an idea, text, etc.) that you didn't come up with yourself, cite it!

By popular vote, laptops/phones should not be used in class except to share something, or to show the reading.

The late policy is: extensions can be requested with a reasonable explanation.

Moderating: everyone should moderate 2 papers, which involves leading the discussion (short summary and open with questions) and asking academic authors the backstory for the paper.

Reading comments: you should make substantive comments for each reading on Slack (adding to the discussion).

Getting help: TA Nedi can help you with the technical parts of the assignments. She has weekly office hours at Wed 11am-1pm in 409 CIT.

Grading