This course aims to teach students about computational ecosystems and community-informed planning. The learning outcomes include understanding the importance of systems thinking in Human-Computer Interaction (HCI), developing regulation skills, and learning about Agile Research Studios. The course covers skills such as collaborative planning, resolving conflicts, and utilizing mixed-initiative interfaces. The teaching method involves lectures and examples to illustrate concepts. The intended audience for this course includes students interested in HCI, community planning, and technology's role in advancing human values.
Overview
Syllabus
Introduction.
Best human solution.
Best machine solution.
Options.
A call for systems: having great components is not enough..
A call for systems thinking in HCI.
Advancing the approach....
Computational ecosystems are systems, designed as integrative solutions.
Rest of the talk.
Challenges for organizers.
Cobi: Community-informed planning.
1. Engage the entire community in the planning process.
Core idea: two-phased collaborative planning process w/ crowds and groups.
Core idea: incentive chaining.
2. Help organizers resolve conflicts.
Core idea: Community-informed mixed-initiative interface.
Computational Ecosystem: Community-Informed Planning.
Students need regulation skills.
Agile Research Studio ARS.
ARS scales faculty time.
ARS is a computational ecosystem for developing regulation skills.
ARS: planning.
Distributed help is not one tool....
Outcomes 3 yrs.
Planning Strategies.
Help & Help-seeking.
Faculty Time: 10-12 hours/week.
Computational Ecosystem: Agile Research Studios.
Regulation skills beyond ARS?.
What's next.
Preview #1: Ecosystem-level architectures.
Example: On-the-go crowdsourcing.
Example: Readily Available Learning Experiences RALE.
a leap: mixed-initiative scaffolds.
Role of technology in advancing human values al scale.
Scaling amplifies compromise.
Delta Lab.
Taught by
Stanford Online
