Context-Aware Prediction of User Performance Problems Caused by the Situationally-Induced Impairments and Disabilities

Akpınar, Mehmet Elgin
When we interact with small screen devices, we can make errors due to our abilities/disabilities, contextual factors that distract our attention, or problems related to the interface. Predicting and learning these errors based on the previous user interaction and contextual factors and adapting the user interface to prevent these errors can improve user performance and satisfaction. This thesis aims to understand how a system can be developed that monitors user performance and contextual changes and predicts performance problems based on context. In this thesis, we first conducted a systematic review to understand the context and its effect on user performance. Then, we conducted a user study in the wild to collect text entry interactions, sensor data, and context labels. First, we used this data to measure user performance regarding typing speed and error rate in an automated system without a predefined task model. Moreover, we investigated how different context dimensions affect user performance. Our findings showed that context affects users differently; therefore, user-specific adaptations should be considered. We also investigated whether different context factors can be sensed using available sensors. Our experiments with machine learning algorithms using available smartphone sensors show that we can differentiate different context factors, particularly mobility and environment. Moreover, we used sensor and user performance data to predict performance problems. The regression model to predict typing errors outperformed the random baseline. Finally, based on these informative studies, we propose adaptation techniques and design guidelines for developers to support user interaction in different contexts.


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Citation Formats
M. E. Akpınar, “Context-Aware Prediction of User Performance Problems Caused by the Situationally-Induced Impairments and Disabilities,” Ph.D. - Doctoral Program, Middle East Technical University, 2022.