TY - GEN
T1 - Affinder
T2 - 2022 CHI Conference on Human Factors in Computing Systems, CHI 2022
AU - Louie, Ryan
AU - Gergle, Darren
AU - Zhang, Haoqi
N1 - Funding Information:
The authors thank Kapil Garg, Garrett Hedman, Spencer Carlson, Dan Rees Lewis, Kristine Lu, Katie Cunningham, Leesha Maliakal Shah, Yongsung Kim, and many other members of the Delta Lab at Northwestern for design feedback and literature help; the students of the DTR program at Northwestern for design and implementation feedback; and Carrie Cai, Andrés Monroy-Hernandez, Jeremy Birnholtz for their thoughtful insights on our contributions. This research was supported in part by a Google Faculty Research Award and by the National Science Foundation under award IIS-1618096.
Publisher Copyright:
© 2022 ACM.
PY - 2022/4/29
Y1 - 2022/4/29
N2 - Context-aware applications have the potential to act opportunistically to facilitate human experiences and activities, from reminding us of places to perform personal activities, to identifying coincidental moments to engage in digitally-mediated shared experiences. However, despite the availability of context-detectors and programming frameworks for defining how such applications should trigger, designers lack support for expressing their human concepts of a situation and the experiences and activities they afford (e.g., situations to toss a frisbee) when context-features are made available at the level of locations (e.g., parks). This paper introduces Affinder, a block-based programming environment that supports constructing concept expressions that effectively translate their conceptions of a situation into a machine representation using available context features. During pilot testing, we discovered three bridging challenges that arise when expressing situations that cannot be encoded directly by a single context-feature. To overcome these bridging challenges, Affinder provides designers (1) an unlimited vocabulary search for discovering features they may have forgotten; (2) prompts for reflecting and expanding their concepts of a situation and ideas for foraging for context-features; and (3) simulation and repair tools for identifying and resolving issues with the precision of concept expressions on real use-cases. In a comparison study, we found that Affinder's core functions helped designers stretch their concepts of how to express a situation, find relevant context-features matching their concepts, and recognize when the concept expression operated differently than intended on real-world cases. These results show that Affinder and tools that support bridging can improve a designer's ability to express their concepts of a human situation into detectable machine representations - thus pushing the boundaries of how computing systems support our activities in the world.
AB - Context-aware applications have the potential to act opportunistically to facilitate human experiences and activities, from reminding us of places to perform personal activities, to identifying coincidental moments to engage in digitally-mediated shared experiences. However, despite the availability of context-detectors and programming frameworks for defining how such applications should trigger, designers lack support for expressing their human concepts of a situation and the experiences and activities they afford (e.g., situations to toss a frisbee) when context-features are made available at the level of locations (e.g., parks). This paper introduces Affinder, a block-based programming environment that supports constructing concept expressions that effectively translate their conceptions of a situation into a machine representation using available context features. During pilot testing, we discovered three bridging challenges that arise when expressing situations that cannot be encoded directly by a single context-feature. To overcome these bridging challenges, Affinder provides designers (1) an unlimited vocabulary search for discovering features they may have forgotten; (2) prompts for reflecting and expanding their concepts of a situation and ideas for foraging for context-features; and (3) simulation and repair tools for identifying and resolving issues with the precision of concept expressions on real use-cases. In a comparison study, we found that Affinder's core functions helped designers stretch their concepts of how to express a situation, find relevant context-features matching their concepts, and recognize when the concept expression operated differently than intended on real-world cases. These results show that Affinder and tools that support bridging can improve a designer's ability to express their concepts of a human situation into detectable machine representations - thus pushing the boundaries of how computing systems support our activities in the world.
KW - block-based programming
KW - bridging challenges
KW - context-aware programming
KW - context-features
KW - design fixation
KW - expressing concepts of situations
UR - http://www.scopus.com/inward/record.url?scp=85130524011&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85130524011&partnerID=8YFLogxK
U2 - 10.1145/3491102.3501902
DO - 10.1145/3491102.3501902
M3 - Conference contribution
AN - SCOPUS:85130524011
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2022 - Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 30 April 2022 through 5 May 2022
ER -