  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 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 has shown potential to enhance 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. Our method promotes consistent grading and enables
instructors to dedicate more focus to assist students in gaining visualization
knowledge and experience. We have successfully deployed our method and
auto-graded D3 submissions from more than 1000 undergraduate and graduate
students in Georgia Tech's CSE6242 Data and Visual Analytics course, and
received positive feedback and encouragement for expanding its adoption.
