VisGrader: Automatic Grading of D3 Visualizations

Matthew Hull, Vivian Pednekar, Hannah Murray, Nimisha Roy, Emmanuel Tung, Susanta Kumar Routray, Connor Guerin, Justin Lu Chen, Zijie J. Wang, Seongmin Lee, Max Mahdi Roozbahani, Duen Horng Chau

Room: 106

2023-10-25T04:57:00ZGMT-0600Change your timezone on the schedule page
Exemplar figure, described by caption below
VisGrader: Automatic Grading of D3 Visualizations
Fast forward
Full Video

Automatic grading, D3 visualization, large class, Selenium, Gradescope grading platform


Manually grading D3 data visualizations is a challenging endeavor, and is especially difficult for large classes with hundreds of students. Grading an interactive visualization requires a combination of interactive, quantitative, and qualitative evaluation that are conventionally done manually and are difficult to scale up as the visualization complexity, data size, and number of students increase. We present VISGRADER, a first-of-its kind automatic grading method for D3 visualizations that scalably and precisely evaluates the data bindings, visual encodings, interactions, and design specifications used in a visualization. Our method enhances students’ learning experience, enabling them to submit their code frequently and receive rapid feedback to better inform iteration and improvement to their code and visualization design. We have successfully deployed our method and auto-graded D3 submissions from more than 4000 students in a visualization course at Georgia Tech, and received positive feedback for expanding its adoption.