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Towards a General Model of Repeated App Usage

Prezenski, Sabine; Russwinkel, Nele; Students of MuSiMMs 2013/14 (Contributor)

The main challenge of implementing cognitive models for usability testing lies in reducing the modeling effort, while including all relevant cognitive mechanisms, such as learning and relearning, in the model. In this paper we introduce a general cognitive modeling approach with ACT-R for hierarchical, list-based smartphone apps. These apps support the task of selecting a target, via navigating through subtargets positioned on different layers. Mean target selection time for repeated app interaction, learning and relearning behavior was collected in four studies conducted with either a shopping app or a real-estate app. The predictions of the general modeling approach match the empirical data very well, both in terms of trends and absolute values. We also explain how such a general modeling approach can be followed. The presented general model approach requires little modeling effort to be used for predicting overall efficiency of other apps. It supports more complex interface, as well.