Math Augmentation: How Authors Enhance the Readability of Formulas using Novel Visual Design Practices

要旨

With the increasing growth and impact of machine learning and other math-intensive fields, it is more important than ever to broaden access to mathematical notation. Can new visual and interactive displays help a wider readership successfully engage with notation? This paper provides the first detailed qualitative analysis of math augmentation—the practice of embellishing notation with novel visual design patterns to improve its readability. We present two qualitative studies of the practice of math augmentation. First is an analysis of 1.1k augmentations to 281 formulas in 47 blogs, textbooks, and other documents containing mathematical expressions. Second is an interview study with 12 authors who had previously designed custom math augmentations ("maugs"). This paper contributes a comprehensive inventory of the kinds of maugs that appear in math documents, and a detailed account of how authors’ tools ought to be redesigned to support efficient creation of math augmentations. These studies open a critical new design space for HCI researchers and interface designers.

受賞
Best Paper
著者
Andrew Head
Allen Institute for AI, Seattle, Washington, United States
Amber Xie
UC Berkeley, Berkeley, California, United States
Marti Hearst
UC Berkeley, Berkeley, California, United States
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3501932

動画

会議: CHI 2022

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)

セッション: Authoring Data

283–285
4 件の発表
2022-05-03 18:00:00
2022-05-03 19:15:00