Experience buffers: a socially appropriate, selective archiving tool for evidence-based care

Authors:
Gillian R. Hayes;Khai N. Truong;Gregory D. Abowd;Trevor Pering
Affiliations:
Georgia Institute of Technology, Atlanta, GA;Georgia Institute of Technology, Atlanta, GA;Georgia Institute of Technology, Atlanta, GA;Intel Research, Santa Clara, CA
Venue:
CHI '05 Extended Abstracts on Human Factors in Computing Systems
Year:
2005

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Abstract

Diagnosis, treatment, and monitoring of interventions for children with autism can profit most when caregivers have substantial amounts of data they can easily record and review as evidence of specific observed behaviors over time. Through our work with one prototype system and interviews with caregivers, we have recognized the importance of socially appropriate ways to add rich data to the information recorded by caregivers. Analysts must be able to view incidents as they occurred without unnecessarily burdening caregivers and other children with always-on recording of data about them. In this paper, we introduce experience buffers, a collection of capture services embedded in an environment that, though always on and available, require explicit user action to store an experience.. This creates a way to balance the social, technical, and practical concerns of capture applications.