Facilitating Deep Learning Through Vertical Integration Between Data Visualization Courses Within an Undergraduate Data Visualization Curriculum

Abstract

There is significant research underway on pedagogical approaches to data visualization that include teaching data visualization in various classroom settings, short-form data visualization instruction, the use of active learning, and the use of design patters for teaching and learning for data visualization. Most data visualization courses are taught independent of a major which might explain the lack of research in the area of vertical integration of visualization courses. This paper shares a method for facilitating deep learning through vertical integration between data visualization courses within an undergraduate data visualization curriculum. The data visualization process, concepts and techniques are introduced in a gateway course during students’ second year of study. Students enrolled in the gateway course for the data visualization major collaborate with other data visualization majors taking a senior level course on a visualization challenge with real-world application. The collaboration facilitates deep learning through vertical integration between the two classes. Students from both classes form a team to compete in a data visualization challenge as part of an annual technology conference in Indianapolis, Indiana. Participation in the team is voluntary. Students who compete in the visualization challenge work with faculty mentors from both classes and practice near-peer mentoring. This paper will provide insight into how the curricular constructs of the data visualization process and deep learning combine to facilitate vertical integration between data visualization courses.