Visualizing Topics and Opinions Helps Students Interpret Large Collections of Peer Feedback for Creative Projects-Reference-Cited by-同舟云学术

Visualizing Topics and Opinions Helps Students Interpret Large Collections of Peer Feedback for Creative Projects

Published:2022-12-20 Issue: Volume: Page:
ISSN:1073-0516
Container-title:ACM Transactions on Computer-Human Interaction
language:en
Short-container-title:ACM Trans. Comput.-Hum. Interact.

Author:

Crain Patrick¹,Lee Jaewook¹,Yen Yu-Chun (Grace)²,Kim Joy³,Aiello Alyssa¹,Bailey Brian¹

Affiliation:

1. University of Illinois, Urbana-Champaign, USA

2. University of California, San Diego, USA

3. Adobe Creative Intelligence Lab, USA

Abstract

We deployed a feedback visualization tool to learn how students used the tool for interpreting feedback from peers and teaching assistants. The tool visualizes the topic and opinion structure in a collection of feedback and provides interaction for reviewing providers’ backgrounds. Eighteen teams engaged with the tool to interpret feedback for course projects. We surveyed students (N=69) to learn about their sensemaking goals, use of the tool to accomplish those goals, and perceptions of specific features. We interviewed students (N=12) and TAs (N=2) to assess the tool’s impact on students’ review processes and course instruction. Students discovered valuable feedback, assessed project quality, and justified design decisions to teammates by exploring specific icon patterns in the visualization. The interviews revealed that students mimicked strategies implemented in the tool when reviewing new feedback without the tool. Students found the benefits of the visualization outweighed the cost of labeling feedback.

Publisher

Association for Computing Machinery (ACM)

Subject

Human-Computer Interaction

Link

https://dl.acm.org/doi/pdf/10.1145/3571817

Reference55 articles.

1. Claire Aitchison . 2014. Learning from multiple voices: Feedback and authority in doctoral writing groups . In Writing groups for doctoral education and beyond . Routledge , 67–80. Claire Aitchison. 2014. Learning from multiple voices: Feedback and authority in doctoral writing groups. In Writing groups for doctoral education and beyond. Routledge, 67–80.

2. Crowdboard

3. Robert G Bing-You , Jay Paterson , and Mark A Levine . 1997. Feedback falling on deaf ears: residents ’ receptivity to feedback tempered by sender credibility. Medical teacher 19, 1 ( 1997 ), 40–44. Robert G Bing-You, Jay Paterson, and Mark A Levine. 1997. Feedback falling on deaf ears: residents’ receptivity to feedback tempered by sender credibility. Medical teacher 19, 1 (1997), 40–44.

4. Kirsten R Butcher and Tamara Sumner . 2011. Self-directed learning and the sensemaking paradox. Human–Computer Interaction 26, 1-2 ( 2011 ), 123–159. Kirsten R Butcher and Tamara Sumner. 2011. Self-directed learning and the sensemaking paradox. Human–Computer Interaction 26, 1-2 (2011), 123–159.

5. Juxtapeer