  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.
