We introduce Augmented Math, a machine learning-based approach to authoring
AR explorable explanations by augmenting static math textbooks without
programming. To augment a static document, our system first extracts
mathematical formulas and figures from a given document using optical character
recognition (OCR) and computer vision. By binding and manipulating these
extracted contents, the user can see the interactive animation overlaid onto
the document through mobile AR interfaces. This empowers non-technical users,
such as teachers or students, to transform existing math textbooks and handouts
into on-demand and personalized explorable explanations. To design our system,
we first analyzed existing explorable math explanations to identify common
design strategies. Based on the findings, we developed a set of augmentation
techniques that can be automatically generated based on the extracted content,
which are 1) dynamic values, 2) interactive figures, 3) relationship
highlights, 4) concrete examples, and 5) step-by-step hints. To evaluate our
system, we conduct two user studies: preliminary user testing and expert
interviews. The study results confirm that our system allows more engaging
experiences for learning math concepts.

通过机器学习为无需编程的人员提供以增强现实为基础的数学书籍，通过提取内容并进行操作，使教师和学生可以将现有的数学教材转化为个性化的可交互解释，研究结果表明我们的系统可以提供更具吸引力的数学学习体验。