Quantitative community management
In recent years, communities as wide-ranging as Wikihow to Thunderbird have been surveying participants and using this information to improve the experiences of participants. A variety of open source projects are now tracking contributors to identify where people fall away, and to nudge them forward. In this talk, you will learn the state of the art in community measurement, common mistakes made in surveying, and how to actively use data to improve activity within a project.
This talk will cover the following issues in detail:
* How Wikipedia used A/B testing to improve contribution rejection messages
* Based on entrance/exit surveys from OpenHatch's Open Source Comes to Campus program, what do new contributors know?
* The impact of treating gender as a plain-text field, rather than a drop-down, on the answer rate
* How Ubuntu's Developer Advisory Team tracks, contacts, and nudges new contributors
* How motivations for Thunderbird contributors differ substantially from the FLOSSpols survey
* How to misread your survey data (and tips on avoiding that)
Upon leaving this talk, you will have a solid background in the current state of data collection within open source communities and how to apply those tools to your own project.
Asheesh loves growing camaraderie among geeks. He chaired the Johns Hopkins Association for Computing Machinery and taught Python classes at Noisebridge, San Francisco’s hackerspace. He realizes that most of the work that makes projects successful is hidden underneath the surface.
He has volunteered his technical skills for the UN in Uganda, the EFF, and Students for Free Culture, and is a Developer in Debian. Until recently, he engineered software and scalability at Creative Commons in San Francisco; today, he works at OpenHatch as its executive director.