FRAMES - a framework for adaptable mobile event-contingent self-report studies

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Abstract

With the emergence of smart, sensor-equipped mobile devices, the gathering and processing of data concerning the everyday lifestyles of the people using these devices has become a matter of course within the field of HCI. New standards in mobile technologies provide new opportunities for empirical studies, which allow researchers to explore people’s everyday lives unobtrusively. Furthermore, mobile, sensor-based approaches enhance empirical studies by automatically recognizing events of interest such as the arrival at specific locations. In this paper we describe how end-user development can be used to empower researchers without technical expertise to adjust their empirical studies to the individual dynamics of daily life. To do so, we implemented and evaluated the framework ‘FRAMES’ that allows researchers to flexibly specify and adopt mobile event-contingent self-report studies. The evaluation shows the potential of our framework for spontaneous customizations of the study without the need for redeployment or modification of the application.

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APA

Dax, J., Ludwig, T., Meurer, J., Pipek, V., Stein, M., & Stevens, G. (2015). FRAMES - a framework for adaptable mobile event-contingent self-report studies. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9083, pp. 141–155). Springer Verlag. https://doi.org/10.1007/978-3-319-18425-8_10

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