WaitSuite: Productive Use of Diverse Waiting Moments
Name
tochi16-waitsuite.pdf
Size
4.19 MB
Format
Adobe PDF
Checksum (MD5)
7aeca2337c9a4dceee1314c83ccc434d
Author(s)
Cai, Carrie Jun
Miller, Robert C
Ren, Anji
Date Issued
March 2017
Journal
ACM Transactions on Computer-Human Interaction
Publisher
Association for Computing Machinery (ACM)
Citation
Cai, Carrie J. et al. “WaitSuite: Productive Use of Diverse Waiting Moments.” ACM Transactions on Computer-Human Interaction 24, 1 (March 2017): 1–41 © 2017 Association for Computing Machinery (ACM)
Version
Author's final manuscript
Abstract
The busyness of daily life makes it difficult to find time for informal learning. Yet, learning requires significant time and effort, with repeated exposures to educational content on a recurring basis. Despite the struggle to find time, there are numerous moments in a day that are typically wasted due to waiting, such as while waiting for the elevator to arrive, wifi to connect, or an instant message to arrive. We introduce the concept of wait-learning: automatically detecting wait time and inviting people to learn while waiting. Our approach is to design seamless interactions that augment existing wait time with productive opportunities. Combining wait time with productive work opens up a new class of software systems that overcome the problem of limited time.
In this article, we establish a design space for wait-learning and explore this design space by creating WaitSuite, a suite of five different wait-learning apps that each uses a different kind of waiting. For one of these apps, we conducted a feasibility study to evaluate learning and to understand how exercises should be timed during waiting periods. Subsequently, we evaluated multiple kinds of wait-learning in a two-week field study of WaitSuite with 25 people. We present design implications for wait-learning, and a theoretical framework that describes how wait time, ease of accessing the learning task, and competing demands impact the effectiveness of wait-learning in different waiting scenarios. These findings provide insight into how wait-learning can be designed to minimize interruption to ongoing tasks and maximize engagement with learning.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3044534
