Scaffolding feedback interpretation process for creative work through reflection, paraphrasing, and information visualization

Interpreting and acting on feedback from diverse audiences can be overwhelming. Practitioners who lack the requisite knowledge and experience for processing such feedback often fail to improve their creative work even when supplied with good feedback. In my thesis, I report the results from three experiments testing novel techniques aimed at enhancing the utility of a collection of feedback written by multiple providers with different backgrounds. In the first experiment, we tested the effects of combining a reflection activity and reviewing external feedback for an iterative design task. Participants (N=90) revised their design after a) performing a reflection activity before reviewing feedback, b) performing the reflection after reviewing feedback, c) performing the reflection only, or d) reviewing the feedback only. We found that performing reflection after feedback review led to the largest increase in perceived quality for the revised designs, and performing reflection and feedback review regardless of the order resulted in the most extensive revision. In the second experiment, we investigated the effects of combining paraphrasing and reflection techniques for feedback interpretation. Participants (N=104) created an initial design and revised it after reviewing feedback using one of the four strategies: a) paraphrasing feedback and then reflect, b) paraphrasing without reflection, c)reflection without paraphrasing, and d) reviewing feedback with no activities. We found that combining the two techniques yielded the largest increase in the perceived quality of the posters from the perspectives of both experts and one’s own evaluation of their work, and the increase was larger than performing paraphrasing alone. Reflection without paraphrasing feedback slightly eroded the designer’s confidence in their revised poster. In the third experiment, we explored how the representation of feedback (visualization vs. raw text) affects its interpretation process and identified insights. In this study, we contributed the design of a new class of tool (Decipher) that enables designers to visualize and navigate a collection of feedback using its topic and opinion structure. In a preliminary evaluation (N=20), we found that Decipher helped users feel less overwhelmed during feedback interpretation tasks and better attend to critical issues and conflicting opinions compared to using a typical document-editing tool. My dissertation represents a large step towards helping creators benefit more effectively from the suggestions, encouragement, and knowledge they receive from end-users, peers, mentors, and clients.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Yen, Yu-Chun

Contributors dc:contributor

• Bailey, Brian P
• Karahalious, Karrie
• Huang, Yun
• Dow, Steven P
• Kim, Joy

• feedback
• creativity
• sense-making
• design
• crowdsourcing
• creativity support tools

Statement dc:rights

• Copyright 2021 Yu-Chun Yen

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/113173

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/113173