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.

翻译：人工评分D3数据可视化是一项颇具挑战的任务，尤其对于拥有数百名学生的大规模课堂而言更是如此。评分交互式可视化需要综合进行交互性、定量和定性评估，这些评估传统上依赖人工完成，且随着可视化复杂性、数据量和学生数量的增加而难以扩展。我们提出了VISGRADER，这是一种首创的D3可视化自动评分方法，能够可扩展且精确地评估可视化中使用的数据绑定、视觉编码、交互和设计规范。该方法提升了学生的学习体验，使他们能够频繁提交代码并获得快速反馈，从而更好地完善代码和可视化设计。我们已成功将该方法应用于佐治亚理工学院可视化课程中4000多名学生的D3提交作业自动评分，并获得了广泛推广应用的积极反馈。