Data Visualization Capacity (DVC) Building Tool for Teaching and Learning Data Visualization

Contribution: A pedagogical tool for teaching and learning data visualization. Since data visualization is such an extensive discipline, it is challenging for novice scholars to get started. The data visualization capacity (DVC) tool that we introduce in this work has a two-fold short-term utility: it can be employed as a resource for inexperienced teachers without formal training in pedagogy for developing learning objectives and outcomes for data visualization activities and assignments while also being a guide for novice visualizers learning the data visualization process. Background: Data visualization is one such discipline that is broadly applicable to various subject areas leading to many people, students as well as industry experts, looking to expand their skill set. Our goal is to introduce a complex, iterative process in an easy-to-understand, non-conflicting manner without over-whelming the students. Previous research suggests that interactive learning environments where students use guided tutorials allow even novice learners to solve difficult concepts effectively. Other similar applications for data visualization tend to focus on a specific, advanced concept rather than introductory material. Intended Outcomes: A data visualization capacity tool that acts as a study resource for anyone new to data visualization while supporting capacity building skills of students allowing them to transform data into insight. Application Design: This work extends the activity worksheets for teaching and learning data visualization published in the 2021 IEEE Computer Graphics and Applications special issue on Visualization Education and Teaching Visualization Literacy. We created a web-based application that is easy to access, use and update where the activity worksheets are part of a module based structure. We introduce each stage of the data visualization process in self-contained modules allowing the students to learn, test, self-asses, and review at a self-guided pace. To support teaching data visualization, the tool focuses on building factual and conceptual knowledge to better understand the role of each individual stage in addition to the iterative nature of the process. Findings: The results of the preliminary analysis by introducing the beta version of the tool to students enrolled in a data visualization course as a resource for a hackathon-like activity show that students who used the tool found it to be useful and report that the tool would be a good study resource for anyone new to data visualization. Test groups contained students from various disciplines and a majority of students expressed a positive outlook on the tool.