Augmented Math: Authoring AR-Based Explorable Explanations by Augmenting
  Static Math Textbooks
Neil Chulpongsatorn Mille Skovhus Lunding Nishan Soni Ryo Suzuki
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.