000257393 001__ 257393
000257393 005__ 20240928175236.0
000257393 0247_ $$aG:(GEPRIS)402731241$$d402731241
000257393 035__ $$aG:(GEPRIS)402731241
000257393 040__ $$aGEPRIS$$chttp://gepris.its.kfa-juelich.de
000257393 150__ $$aSPP 2199: Skalierbare Interaktionsparadigmen für allgegenwärtige Rechnerumgebungen.$$y2020 -
000257393 371__ $$aProfessorin Dr. Susanne Boll
000257393 450__ $$aDFG project G:(GEPRIS)402731241$$wd$$y2020 -
000257393 5101_ $$0I:(DE-588b)2007744-0$$aDeutsche Forschungsgemeinschaft$$bDFG
000257393 680__ $$aThe core research question ahead of us is how to make the paradigms of interaction scale to large and complex pervasive computing environments. In the previous section, we introduced exemplary application domains to illustrate the challenges for interaction paradigms in pervasive computing environments and set the leading goal of this Priority Programme. In this section, we will identify three core research areas that we aim to pursue with this program. First and foremost, we aim to study interaction paradigms that are scalable in the sense of overarching large ensembles of interactive devices in pervasive computing environments. We need to investigate how we design and evaluate methods that span devices and physical entities easily (1). However, to evaluate novel interaction paradigms in and across settings, we need to adapt our methods for user studies in pervasive computing environments to give us robust results for experiments in the wild (2) and assess the newapproaches quantitatively (3). 1. Design of efficient and meaningful scalable interaction paradigms for pervasive computing environments: How do existing interaction paradigms scale to pervasive computing environments, i.e., distributed ensembles computational devices? What are the characteristics of interaction paradigms that can be used across devices and domains? How can we ensure that interaction paradigms can be used independently of the context but still consider the context-induced restrictions? Are there fundamental limitations that prevent the adoption of a single pervasive interaction paradigm? How do we address issues of efficiency as well as broader aspects of meaning through these interaction paradigms? 2. Rigorous and robust evaluation of scalable interaction paradigms in pervasive computing environments: How do we evaluate interaction techniques that are supposed to work across a range of devices and domains? Can there be standardized study methods to evaluate interaction paradigms for pervasive computing environments? What are the methods to evaluate interaction paradigms in-situ? How far can we extend unsupervised observation techniques by modern sensor technology to reach a reliable understanding of the usage of pervasive computing environments? Can model-based simulation of user interaction speed up the design phase and enable us to select promising interaction designs early in the design process? 3. Assessment of the success of interaction paradigms in pervasive computing environments: What are the metrics that measure and describe actual success, effectiveness, and satisfaction in large settings of pervasive computing environments? What are the score and value under which we rate a design effective and efficient but also meaningful and pleasant for an individual? What is a good balance between traditional performance metrics such as task performance and error rate versus user experience, joy of use, and well-being? What are meaningful testbeds to verify the results
000257393 909CO $$ooai:juser.fz-juelich.de:923996$$pauthority$$pauthority:GRANT
000257393 909CO $$ooai:juser.fz-juelich.de:923996
000257393 980__ $$aG
000257393 980__ $$aAUTHORITY