Positive Technology Journal

Ambient Intelligence (AmI) systems can be viewed as environments in which people will increasingly live their lives. Ubiquitous AmI technologies and systems like personal digital assistants, wearable sensors, mobile phones challenge traditional usability evaluation methods, because use context can be difficult to recreate in a laboratory setting. This view suggests that the evaluation of user’s experience with AmI systems should take place in realistic contexts, such the workplace, the home, etc. Another issue has to do with the content of the evaluation. Performance-based approaches are not suitable for AmI systems, because it is difficult to specify tasks that capture the complexity of real world activities. Moreover, experience is idiosyncratic, in that it is related to the specific bio-cultural configuration of each individual, and it can undergo changes throughout individual life and daily situations.

The Experience Sampling Method (ESM) offers a new perspective in the analysis of these issues. ESM is based on the online repeated assessment of individual behavior and experience in the daily context. Participants describe themselves and their environment while interacting with it. They carry with them for one week an electronic beeper and a booklet of self-report forms. Whenever they receive an acoustic signal, they are expected to fill out a form. The form contains open-ended questions about situational variables such as place, activities carried out, social context, and subjective variables such as the content of thought, perceived goals, and physical conditions. The form also contains 0-12 Likert-type scales investigating the quality of experience in its various components: affect, motivation, activation, and cognitive efficiency.

Intille and colleagues at MIT have recently developed a Personal Digital Assistant-based version of the ESM which can be used for user-interface development and assessment of ubiquitous computing applications. This approach, called Context-Aware Experience Sampling, includes the possibility to assess user’s experience not only through the standard time-based protocol, but also according to the participant’s location, by means of information provided by a GPS plug-in. Thus, researchers can design experiments collecting self-reports only when the participant is near a location of interest. Moreover, users can answers via audio recording or by taking a picture with a camera.

More to explore

S. S. Intille, J. Rondoni, C. Kukla, I. Ancona, L. Bao, A context-aware experience sampling tool, CHI Extended Abstracts 2003, 972-973.

Gaggioli, A., Optimal Experience in Ambient Intelligence (2005), in Ambient Intelligence, Riva, G., Vatalaro, F., Davide, F., Alcañiz, M. (Eds.), Amsterdam: IOS Press. PDF