A context-aware experience sampling tool

Abstract

A new software tool for user-interface development and assessment of ubiquitous computing applications is available for CHI researchers. The software permits researchers to use common PDA mobile computing devices for experience sampling studies. The basic tool offers options not currently available in any other open-source sampling package. However, the tool also has one a completely new type of functionality: context-aware experience sampling. This feature permits researchers to acquire feedback from users only in particular situations that are detected by sensors connected to a mobile computing device. KeywordsContext-aware, experience sampling, reflection, eliciting preferences, PDA, ubiquitous and mobile computing. THE PROBLEMUser needs are typically elicited via personal or focus group interviews, site visits, and photographic and video analysis. Often, however, users know more than they say in a single or even several interviews [1]. As user interface design moves off the desktop and into the real world, two new challenges for designers emerge: (1) developing realistic task specifications that respond to the complexity of fastchanging, real world activities, and (2) evaluating new technologies in realistic contexts. Desktop computing applications can be designed and evaluated using controlled, laboratory observation because most user interface design has nothing to do with physical space [2]. Developers of ubiquitous and mobile computing applications for the home and workplace, however, currently lack a powerful and economical assessment toolset that accounts for user activity in a broader context. The behavior of the people and their response to technology is critically dependent upon the environment and context in which information is presented or requested.The most popular assessment instruments in use today for studying the activities of people in natural settings are self report recall surveys, time diaries, direct field observation, and experience sampling. Self-report recall surveys suffer from recall and selective reporting biases -users can often not remember what they did. Time diaries, where users write down what they do during the day, are burdensome for the user. Although direct field observation can provide helpful qualitative and quantitative measures, it is costly, timeconsuming, and disruptive and therefore not practical for many design tasks. The experience sampling method (ESM) has been used primarily for time-use analysis [3] and only recently for interface design [4,5]. Subjects carry a beeper device that "samples" for information on some predetermined schedule. When the device beeps, subjects answer questions of interest to the researchers. With a sufficient number of subjects and samples, a statistical model of behavior can be generated. The ESM is less susceptible to subject recall errors than other self-report feedback elicitation methods [3], but it high sampling rates interrupt activities of interest and irritate subjects. Image-based experience sampling alleviates these ...

A large-scale study of daily information needs captured in situ

“…The ESM is a research method that tackles this issue explicitly by asking participants to report on their experiences at specific points throughout the day. The method was originally developed in the psychology domain [Barrett and Barrett 2001] and has been adapted successfully in many studies of HCI [Intille et al 2003;Consolvo and Walker 2003;Iachello et al 2006;Fischer 2009]. As highlighted previously [Cherubini and Oliver 2009], the main advantage of ESM is its ability to preserve the ecological validity [Hormuth 1986].…”

Survey of Context Provisioning Middleware

“…The Context-Aware Experience Sampling Tool (CAES) [111] addresses HCI researchers and allows them to acquire feedback from users in particular situations detected by sensors. It runs on PocketPC devices and supports context-aware alarm triggering for the ESM.…”

GPARS: a general-purpose activity recognition system

“…Listening music 0(2) 0(0) 3(6) 0(1) 5(1) 0(0) 9 (7) of the one-layer classifier is worse in comparison with the two-layer classifier. The two-layer classifier yields significant improvement (in some cases it is more than 30%) in comparison with the one-layer classifier.…”

“…The dataset to train an AR system is usually obtained through annotation. Contextaware experience sampling tool (ESM) [6][7][8] is the typical annotation technique. When using ESM, the participants either carry a PDA (with an ESM software installed on it) or wear a headset to report the current persuasion of an activity to a server (with an ESM software installed on it).…”