Computer Science Department, PhD Proposal Defense , Yiren Ding "Assessments, Modeling, and Platforming for Data Visualization Literacy"

Yiren Ding

PhD Student

WPI – Computer Science Department 

Friday, November 22, 2024

Time: 10:30 a.m. – 11:30 a.m.

Location: Zoom  https://wpi.zoom.us/j/98230942832 

Committee Members:

Advisor: Prof Lane Harrison, WPI – Computer Science

Prof. Erin Solovey, WPI - Computer Science

Prof. Stacy Shaw , WPI -  Social Science & Policy Studies

Prof. Alex Lex, University of UTAH, Computer Science

Abstract:

Data visualization is a powerful tool that conveys information through graphical representations, facilitating a more accessible and efficient exploration of data and ultimately aiding in the discovery of valuable insights. Data visualization literacy is an essential cognitive skill for making informed decisions, communication, and information exploration through data visualization. However, measuring and improving an individual’s data visualization literacy is a challenging and undeveloped area. Conducting empirical studies is a feasible and promising method to gain insights into people’s data visualization literacy. However, conducting empirical studies on data visualization presents inherent complexity due to the sophisticated and nuanced nature of stimuli, coupled with the flexibility of experimental procedures. Simultaneously, the traditional “average observer” analysis often yields limited insights into individual performance, making it challenging to offer tailored models.

his dissertation aims to address these challenges through four primary research tasks: (1) Expanding and enhancing data visualization literacy experiments and modeling; (2) Designing assessments and interventions to improve individual data visualization literacy; (3) Developing a framework to support researchers in building flexible data visualization empirical studies; and (4) Designing and evaluating how educational platforms might be an effective mechanism for promoting data visualization literacy. By completing these tasks, our outcomes may inform and impact efforts across data visualization research, practice, and education. 

The framework we have developed for constructing data visualization experiments reduces barriers and expedites empirical studies. Finally, we developed a platform that could serve as a hub for online data visualization education, and we explored people’s engagement and motivation for self-learning visualization literacy. In summary, this dissertation will significantly advance research in data visualization across modeling, intervention, assessment, tooling, and platforming.