| Title | Leveraging Peer Feedback to Improve Visualization Education |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Beasley, Zachariah, Friedman, Alon, Pieg, Les, Rosen, Paul |
| Conference Name | 2020 IEEE Pacific Visualization Symposium (PacificVis) |
| Keywords | codes, Computer languages, Computer science, concepts and paradigms, Education, encoding, evaluation methods, Grammar, human factors, human-centered computing, pubcrawl, Reinforced Learning from Human Feedback, resilience, Resiliency, Scalability, visualization, Visualization design, Visualization theory |
| Abstract | Peer review is a widely utilized pedagogical feedback mechanism for engaging students, which has been shown to improve educational outcomes. However, we find limited discussion and empirical measurement of peer review in visualization coursework. In addition to engagement, peer review provides direct and diverse feedback and reinforces recently-learned course concepts through critical evaluation of others' work. In this paper, we discuss the construction and application of peer review in a computer science visualization course, including: projects that reuse code and visualizations in a feedback-guided, continual improvement process and a peer review rubric to reinforce key course concepts. To measure the effectiveness of the approach, we evaluate student projects, peer review text, and a post-course questionnaire from 3 semesters of mixed undergraduate and graduate courses. The results indicate that course concepts are reinforced with peer review--82% reported learning more because of peer review, and 75% of students recommended continuing it. Finally, we provide a road-map for adapting peer review to other visualization courses to produce more highly engaged students. |
| Notes | ISSN: 2165-8773 |
| DOI | 10.1109/PacificVis48177.2020.1261 |
| Citation Key | beasley_leveraging_2020 |
Leveraging Peer Feedback to Improve Visualization Education