论文标题
利用同行反馈来改善可视化教育
Leveraging Peer Feedback to Improve Visualization Education
论文作者
论文摘要
同行评审是一种广泛使用的教学反馈机制，可用于吸引学生，这已被证明可以改善教育成果。但是，我们发现在可视化课程中对同行评审的讨论和经验测量有限。除了参与外，同行评审还提供了直接和多样化的反馈，并通过对他人的工作进行批判性评估来加强最近学习的课程概念。在本文中，我们在计算机科学可视化课程中讨论了同行评审的构建和应用，包括：在反馈引导，持续的改进过程中重复使用代码和可视化的项目，以及同行评审的标题，以增强关键课程概念。为了衡量该方法的有效性，我们评估了学生项目，同行评审文本以及三个学期混合本科和研究生课程的课后问卷。结果表明，随着同行评审的加强，课程概念得到了加强--- 82％的人报告了由于同行评审而进行的学习更多，而75％的学生建议继续学习。最后，我们提供了一个路线图，以调整同行评审到其他可视化课程，以培养更多高度参与的学生。
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.
