Predicting task execution times by deriving enhanced cognitive models from user interface development models

Abstract

Adaptive user interfaces (UI) offer the opportunity to adapt to changes in the context, but this also poses the challenge of evaluating the usability of many different versions of the resulting UI. Consequently, usability evaluations tend to become very complex and time-consuming. We describe an approach that combines model-based usability evaluation with development models of adaptive UIs. In particular, we present how a cognitive user behavior model can be created automatically from UI development models and thus save time and costs when predicting task execution times. With the help of two usability studies, we show that the resulting predictions can be further improved by using information encoded in the UI development models.

12th Biannual conference of the German cognitive science society (Gesellschaft für Kognitionswissenschaft)

“…Individual slopes for Fitts' difficulty (ID) ranged from 121 to 210 ms/bit 7 The experiment as described here was embedded in a larger usability study. See Quade et al (2014) for more details. The instructions are available for download at http://www.tu-berlin.…”

“…by creating simple cognitive models using CogTool (John et al 2004), the additional information encoded into the abstract UI model allows to go much further. It contains machine readable knowledge about the application logic and the UI elements that are to be visited to attain a specified goal, which creates a significant opportunity for machine translation into more precise cognitive models (Quade et al 2014). In this talk, I will show how completion time predictions can be improved based on abstract UI model information.…”