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数据可视化是一项具有挑战性的工作，尤其对于包含数百名学生的大规模课程而言更是如此。交互式可视化的评分需要综合交互式、定量和定性评估，这些评估传统上均以人工方式完成，且随着可视化复杂度、数据规模和学生数量的增加，难以实现规模化扩展。我们提出VisGrader——首个针对D3可视化的自动评分方法，该方法能够可扩展且精准地评估可视化中使用的数据绑定、视觉编码、交互方式及设计规范。本方法能够增强学生的学习体验，使他们能够频繁提交代码并快速获得反馈，从而更好地进行代码和可视化设计的迭代与改进。我们已在佐治亚理工学院一门可视化课程中成功部署该方法，并对4000余名学生的D3提交作业进行了自动评分，获得了积极的反馈，推动了该方法更广泛的推广应用。