Wide-Field Ethnography: Studying Collaboration in the Wild
Collaboration is increasingly important for the deep, rapid, and interdisciplinary learning and innovation needed to address highly complex “wicked” problems, such as global challenges. To deeply understand collaboration, we need to be able to analyze the highly-contextual resources (speech, gestures, inscriptions, body orientation, …) that people make available to others as they collaborate. Gathering and analyzing such multi-modal data is surprisingly difficult, impeding our ability to learn about and improve our ability to collaborate and build tools and processes to enhance collaboration.
Goal
Create and evolve a set of principles, practices, and tools to help researchers gather, store, organize, analyze, and share these datasets.
Student Qualifications
- Self-motivated, responsible, strong team player,
- Good communication skills,
- Commit to the project for two quarters,
- Technical skills (one or more): Solid Python programming skills, and a strong interest in analyzing data (transcribing, annotating, coding, etc.
Student Responsibilities
- Develop and maintain high-quality open-source research software.
- Produce high-quality analysis data, and attend weekly research meetings.