Leveraging multidisciplinarity in a visual analytics graduate course

Area of Science:

• Computer Science
• Data Science
• Information Science

Background:

• Growing demand for visual analytics (VA) expertise across engineering, business, science, research, and industry.
• Teaching VA is complex due to its multidisciplinary nature, diverse student backgrounds, and instructor requirements.
• Existing pedagogical approaches may not fully address the unique challenges of VA education.

Purpose of the Study:

• To report best practices for teaching visual analytics at the graduate level.
• To demonstrate how to leverage the inherent challenges in VA education as pedagogical opportunities.
• To provide insights for developing effective VA curricula and instructional strategies.

Main Methods:

• Case study of a graduate-level visual analytics course at Purdue University.
• Analysis of how course instructors adapted to and utilized student diversity and multidisciplinary content.
• Documentation of specific teaching strategies and their outcomes.

Main Results:

• Challenges in VA education were reframed as opportunities for enhanced learning.
• Curriculum design and instructional methods were adapted to foster interdisciplinary understanding.
• Students developed practical visual analytics expertise by engaging with complex, real-world problems.

Conclusions:

• Effective visual analytics education requires embracing, rather than mitigating, multidisciplinary and diversity challenges.
• Best practices involve innovative pedagogical approaches tailored to the unique demands of visual analytics.
• This approach can successfully cultivate essential visual analytics skills for diverse industries.