Mixed Abilities and Varied Experiences: A Group Autoethnography of a Virtual Summer Internship.
Saved in:
| Title: | Mixed Abilities and Varied Experiences: A Group Autoethnography of a Virtual Summer Internship. |
|---|---|
| Authors: | Mack, Kelly1 kmack3@uw.edu, Das, Maitraye1 maitraye@uw.edu, Jain, Dhruv2 profdj@umich.edu, Bragg, Danielle3 danielle.bragg@microsoft.com, Tang, John4 johntang@microsoft.com, Begel, Andrew5 abegel@cmu.edu, Beneteau, Erin1 ebenet@uw.edu, Davis, Josh Urban6 josh.u.davis.gr@dartmouth.edu, Glasser, Abraham7,8 atg2036@rit.edu, Joon Sung Park9,10 joonspk@stanford.edu, Potluri, Venkatesh1 vpotluri@cs.washington.edu |
| Source: | Communications of the ACM. Aug2023, Vol. 66 Issue 8, p105-113. 9p. 1 Chart. |
| Subjects: | Internship programs, Telecommuting, Accessible design, Microsoft Corp., Research teams, Work environment, COVID-19 pandemic |
| Abstract: | The COVID-19 pandemic forced many people to convert their daily work lives to a “virtual” format, in which they connected remotely from home. In this new, virtual environment, accessibility barriers changed, in some respects for the better (e.g., more flexibility) and in other aspects, for the worse (e.g., problems including American Sign Language interpreters over video calls). Microsoft Research held its first cohort of all virtual interns in 2020. We, the interns, full-time members, and affiliates of the Ability Team, a Microsoft research team focused on accessibility, report on our experiences navigating the challenges of working remotely. We constituted a variety of abilities, positions, and levels of seniority. Using an autoethnographic method, we provide a nuanced view of how the virtual setting affected the experiences of our mixed-ability team, the strategies we used to improve access, and areas for further improvement. We close by presenting guidelines for future virtual mixedability teams to improve workplace accessibility. [ABSTRACT FROM AUTHOR] |
| Copyright of Communications of the ACM is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
Be the first to leave a comment!
