Abstract. Emotion is an important mental and physiological state, influencing cognition, perception, learning, communication, decision making, etc. It is considered as a definitive important aspect of user experience (UX), although at least well developed and most of all lacking experimental evidence. This paper deals with an application for emotion detection in usability testing of software. It describes the approach to utilize the valence arousal space for emotion modeling in a formal experiment. Our study revealed correlations between low performance and negative emotional states. Reliable emotion detection in usability tests will help to prevent negative emotions and attitudes in the final products. This can be a great advantage to enhance Universal Access.
This work follows an ongoing discussion on the implications of skeuomorphic vs. flat design for interface design. Therefor two subsets of the standard iOS6 and iOS7 system icons were reviewed with a semiotic inspection method and compared against each other. The subsets were chosen according to an open online user rating. The findings suggest that missing information due to design simplification is a major issue for less user acceptance. This study shows that especially flat design affords a more careful focus on the semantics of the used elements.
Information overload refers to the state of having too much information to make a decision or remain informed about a topic. We present a novel approach of filtering, adapting and visualizing content inside a Wiki knowledge base. Thereby we follow the question of how to optimize the process of learning, with respect to shorter time and higher quality, in face of increasing and changing information. Our work adopts a consolidation mechanism of the human memory, in order to reveal and shape key structures of a Wiki hypergraph. Our hypothesis so far is that visualization of these structures enables a more efficient learning.
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