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
DOI: 10.1109/TVCG.2023.3327181
Room: 106
2023-10-25T04:57:00ZGMT-0600Change your timezone on the schedule page
2023-10-25T04:57:00Z
Fast forward
Full Video
Keywords
Automatic grading, D3 visualization, large class, Selenium, Gradescope grading platform
Abstract
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.