Exploring Team-Sourced Hyperlinks to Address Navigation Challenges for Low-Vision Readers of Scientific Papers

Author:

Park Soya1,Bragg Jonathan2,Chang Michael3,Larson Kevin4,Bragg Danielle5

Affiliation:

1. Massachusetts Institute of Technology, Cambridge, MA, USA

2. Allen Institute for Artificial Intelligence, Seattle, WA, USA

3. Stanford University, Stanford, CA, USA

4. Microsoft Research, Seattle, WA, USA

5. Microsoft Research, Cambridge, MA, USA

Abstract

Reading academic papers is a fundamental part of higher education and research, but navigating these information-dense texts can be challenging. In particular, low-vision readers using magnification encounter additional barriers to quickly skimming and visually locating information. In this work, we explored the design of interfaces to enable readers to: 1) navigate papers more easily, and 2) input the required navigation hooks that AI cannot currently automate. To explore this design space, we ran two exploratory studies. The first focused on current practices of low-vision paper readers, the challenges they encounter, and the interfaces they desire. During this study, low-vision participants were interviewed, and tried out four new paper navigation prototypes. Results from this study grounded the design of our end-to-end system prototype Ocean, which provides an accessible front-end for low-vision readers, and enables all readers to contribute to the backend by leaving traces of their reading paths for others to leverage. Our second study used this exploratory interface in a field study with groups of low-vision and sighted readers to probe the user experience of reading and creating traces. Our findings suggest that it may be possible for readers of all abilities to organically leave traces in papers, and that these traces can be used to facilitate navigation tasks, in particular for low-vision readers. Based on our findings, we present design considerations for creating future paper-reading tools that improve access, and organically source the required data from readers.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Human-Computer Interaction,Social Sciences (miscellaneous)

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