Designing crowdsourcing techniques based on expert creative practice
Abstract/Contents
- Abstract
- Platforms like Amazon Mechanical Turk have made it possible for hundreds of people to come together to produce creative work at massive scale. Complex work is often managed by breaking down a large task into smaller subtasks; the sub-results produced by these subtasks are combined to create the final result. Projects like the Johnny Cash Project, for example, asked workers to draw individual frames which were combined to create a reinterpretation of a music video. Another commonly used technique to manage crowdsourced work is to build a workflow that connects individual tasks through concretely defined inputs and outputs. By splitting a larger work into smaller parts that can be completed independently, these crowdsourced workflows can often accomplish work more quickly or accurately than an individual. However, these techniques can only be applied to certain kinds of work: the task must be divisible into independent subtasks, which often means that subtasks are designed to help accomplish a predetermined desired outcome. As a result, crowds have difficulty completing tasks with complex interdependent parts (such as writing stories or composing music), because modifying one section may require changes in other sections of the work. Rather than splitting complex work into independent steps, what if we could enable the crowd to engage in a creative process that looks more like that of an expert? By doing this, we may expand the crowd's ability to work together on complex creative projects. This dissertation takes inspiration from expert creative practices to design new crowdsourcing and social computing techniques for accomplishing complex creative work online. These techniques are developed through three systems: Ensemble, Mechanical Novel, and Mosaic. In Ensemble, I use the practice of developing and revising constraints to split the task of writing a short fiction between a leader (who defines constraints and goals for a story) and the crowd (who executes works based on these goals). Mechanical Novel expands on this by studying how crowds can define high-level goals for themselves, without needing a central leader to coordinate work. Lastly, Mosaic generalizes this idea of designing collaborative systems based on expert practices by exploring design affordances for an online community that values non-traditional success outcomes such as early work, failure, and experimentation. These systems demonstrate how expert creative practices can be built into the design of collaborative creativity support tools, and show how designing for exploration can result in better creative outcomes than just focusing on success.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2017 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Kim, Joy Oakyung | |
|---|---|---|
| Associated with | Stanford University, Department of Computer Science. | |
| Primary advisor | Bernstein, Michael | |
| Thesis advisor | Bernstein, Michael | |
| Thesis advisor | Agrawala, Maneesh | |
| Thesis advisor | Dontcheva, Mira | |
| Advisor | Agrawala, Maneesh | |
| Advisor | Dontcheva, Mira |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Joy Oakyung Kim. |
|---|---|
| Note | Submitted to the Department of Computer Science. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2017. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2017 by Joy Oakyung Kim
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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