Developing and Evaluating a Toolkit and Curriculum for Teaching and Learning Data Visualization

可视化是跨STEM领域分析数据不可或缺的方法。虽然可视化研究已经推进了至少30年，但本科层次的可视化教育却相对滞后。可视化教科书是近十年才出现的，几乎没有教学软件工具来辅助数据可视化的教与学。这个项目旨在开发一个工具包，让大学生学习可视化概念和算法。该工具包称为VisVisual，由四个工具组成：VolumeVisualTM、FlowVisualTM、GraphVisualTM和TreeVisualTM。总而言之，这些工具涵盖科学可视化(标量和矢量场可视化)和信息可视化(图形和树绘制)。模块化设计将允许教师只将相关组件融入他们的教学中。为了支持主动学习，每个工具都将向学生提供即时反馈，自动评分组件将检查学生的理解情况。将收集评估和评估数据，以了解学生如何使用这些工具以及这些工具对他们学习的影响。这些信息将指导VisVision的改进和其他可视化工具的开发。预计提高学生的数据可视化能力将增加他们对STEM的兴趣，并吸引他们中的一些人进入数据科学和可视化科学领域。VisVisual将专注于帮助学生学习科学可视化和信息可视化中的概念和算法。为促进数据可视化教育，将开发和公开共享支持使用VisVisual的课程材料，包括数据可视化课程的教案、测试题、数据集和基于网络的教程。预计与课程材料一起提供VisVisual软件将增加世界各地的教育工作者对其的使用。VisVision的有效性将通过受控用户研究获得的数据进行评估，在受控用户研究中，学生完成分配的任务并回答与这些任务相关的问题。为了比较受试者群体或参数设置之间的表现差异，研究人员将应用学生t检验、方差分析和其他统计分析。将获得有关学生的信息(例如，机构类型；专业)，并将使用多变量变异分析来确定这些个人因素如何影响表现。这些信息将被用来更好地理解VisVisual组件在学生理解他们使用VisVision探索的概念方面所起的作用。计划对K-12社区的扩展将向教师和学生介绍视觉设计的原则及其在数据科学中所起的重要作用。VisVision将在项目网站上免费提供，从而实现广泛传播。计划在全国会议上举办一次研讨会，帮助计算机科学教师学习如何在课堂上使用它。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Visualization is an indispensable method for analyzing data across STEM fields. Although visualization research has advanced for at least thirty years, visualization education at the undergraduate level has lagged behind. Visualization textbooks only emerged in the past decade, and few pedagogical software tools are available to assist the teaching and learning of data visualization. This project aims to develop a toolkit that will engage college students in learning visualization concepts and algorithms. This toolkit, called VisVisual, consists of four tools: VolumeVisual, FlowVisual, GraphVisual, and TreeVisual. Together, these tools cover scientific visualization (scalar and vector field visualization) and information visualization (graph and tree drawing). The modular design will allow instructors to incorporate only relevant components into their teaching. To support active learning, each tool will provide instant feedback to students and an auto-grading component will check for student understanding. Assessment and evaluation data will be gathered to understand how students use the tools and the impact of the tools on their learning. This information will guide improvements to VisVisual and the development of additional visualization tools. It is expected that improving students' ability to visualize data will increase their interest in STEM and attract some of them to the scientific field of data science and visualization.VisVisual will focus on helping students learn concepts and algorithms in scientific visualization and information visualization. To promote data visualization education, curriculum materials to support the use of VisVisual will be developed and openly shared, including lesson plans for a data visualization course, test questions, datasets, and web-based tutorials. It is expected that providing the VisVisual software along with the curriculum materials will increase its use by educators across the world. The effectiveness of VisVisual will be evaluated by data obtained from a controlled user study in which students complete assigned tasks and answer questions related to those tasks. To compare performance differences between subject groups or parameter settings, the researchers will apply Student's t-test, analysis of variance, and other statistical analyses. Information about students (e.g., type of institution; major) will be obtained and multivariate analysis of variation will be used to determine how these individual factors affect performance. This information will be used to better understand the role of VisVisual components on students' understanding of the concepts that they explore using VisVisual. Planned outreach to the K-12 community will introduce teachers and students to the principles of visual design and the important role it plays in data science. VisVisual will be freely available on the project website, thus enabling wide dissemination. A workshop at a national meeting is planned, to help computer science faculty learn how to use it in their classes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.