Living Lab researchers face the challenge of measuring behavior in natural environments in an efficient manner. This is especially important when they want to evaluate technology in situ and over long stretches of time. This context aware Living Lab research will require supporting tools and strategies. 

    The current methods to capture the naturalistic setting require a tremendous amount of resources if the researcher wants to capture multiple snapshots of the context in which the interaction with technology takes place. Currently there are 5 types of methods being used by Living lab practitioners to evaluate interactions in the field. Interviews are performed individually or in group and are effective for gathering general information on the users tasks. Self report studies or recall surveys that ask a user to assess their behavior in a naturalistic context. Direct observation, where a researcher follows the user around and literally observes what he or she is doing, does not suffer from selective and recall bias like interviews and surveys do, but it is very costly because it is time-consuming and disruptive if the natural setting is a home setting for example. To minimize the selective and recall bias, sometimes diaries are used as well. 

   The users are then asked to write down what they do during the day as they are doing it or at certain time intervals. This is more burdensome for the user itself. Experience sampling is a form of diary method, where users are prompted to self-report diary entries. ESM (=experience sampling) is less susceptible to subject recall errors than other self-report feedback elicitation methods, but the primary drawback to ESM is the interruption created by the sampling itself. ESM disrupts the user’s activity, requiring the user to stop the current activity and answer questions. Especially because often high sampling rates are used to discover activities of interest which can become burdensome for the user and subject-selection bias. (Intille et al, 2003). 

   This clearly shows that Living Lab researchers lack a powerful and economical assessment tool. In response to this, we have been looking at the potential of context aware experience sampling. This technique tries to remove the burden of experience sampling with the user, by triggering/prompting question based on the context in which the user is. In addition to extended data collection capabilities, this methodology also provides one fundamentally new type of functionality: context-aware experience sampling. 

   This permits researchers to acquire feedback from users only in particular situations that are detected by sensors connected to a mobile computing device or placed in the environment. In other words, the context (location, time, event, biosensor data) can trigger the sampling. This workshop will introduce CAES to living lab practitioners as well as showcase a tool that can support them in applying this methodology.