Leveraging Peer Feedback to Improve Visualization Education

2020 IEEE Pacific Visualization Symposium (PacificVis) Pub Date : 2020-01-12 DOI:10.1109/PacificVis48177.2020.1261

Zachariah J. Beasley, A. Friedman, L. Piegl, Paul Rosen

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引用次数: 6

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.

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利用同伴反馈改善可视化教育

同行评议是一种广泛应用于学生参与的教学反馈机制，已被证明可以改善教育成果。然而，我们发现在可视化课程中同行评议的讨论和实证测量有限。除了参与之外，同行评议还提供直接和多样化的反馈，并通过对他人工作的批判性评价来强化最近学到的课程概念。在本文中，我们讨论了同行评议在计算机科学可视化课程中的构建和应用，包括:在反馈引导的持续改进过程中重用代码和可视化的项目以及加强关键课程概念的同行评议大纲。为了衡量该方法的有效性，我们评估了学生项目、同行评议文本以及来自本科和研究生混合课程的3个学期的课程后问卷。结果表明，同行评议强化了课程概念——82%的学生表示，由于同行评议，他们学到了更多东西，75%的学生建议继续这样做。最后，我们提供了一个路线图，使同行评议适应其他可视化课程，以培养更多的高度参与的学生。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2020 IEEE Pacific Visualization Symposium (PacificVis)

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