On-the-Fly Usability Evaluation of Mobile Adaptive UIs Through Instant User Feedback

Yigitbas, Enes; Jovanovikj, Ivan; Josifovska, Klementina; Sauer, Stefan; Engels, Gregor

doi:10.1007/978-3-030-29390-1_38

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…It is important to note that further validation through testing with users is necessary to establish the full effectiveness of this approach. In particular, future evaluations of the HMI should consider the impact of interface adaptations on end-user satisfaction [6], incorporating a combination of machine learning metrics, user feedback, and usability metrics such as click-through rate and timeon-task [25].…”

Section: Figure 4: Incremental Sequence Evaluationmentioning

confidence: 99%

Exploring the transformation of user interactions to Adaptive Human-Machine Interfaces

Carrera-Rivera,

Reguera-Bakhache,

Larrinaga

et al. 2023

XXIII International Conference on Human Computer Interaction

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Human-machine interfaces (HMI) facilitate communication between humans and machines, and their importance has increased in modern technology. However, traditional HMIs are often static and do not adapt to individual user preferences or behavior. Adaptive User Interfaces (AUIs) have become increasingly important in providing personalized user experiences. Machine learning techniques have gained traction in User Experience (UX) research to provide smart adaptations that can reduce user cognitive load. This paper presents an ongoing exploration of a method for generating adaptive user interfaces by analyzing user interactions and contextual data. It also provides an illustrative example using Markov chains to predict the next step for users interacting with an app for an industrial mixing machine. Furthermore, the paper conducts an offline evaluation of the approach, focusing on the precision of the recommendations. The study emphasizes the importance of incorporating user interactions and contextual data into the design of adaptive HMIs, while acknowledging the existing challenges and potential benefits.

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Section: Figure 4: Incremental Sequence Evaluationmentioning

confidence: 99%

Exploring the transformation of user interactions to Adaptive Human-Machine Interfaces

Carrera-Rivera,

Reguera-Bakhache,

Larrinaga

et al. 2023

XXIII International Conference on Human Computer Interaction

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“…However, the acceptance of each UI adaptation feature should be evaluated at the very moment and in context-of-use when the adaptation is triggered at runtime. To address this issue, in our previous work [36], we introduced an on-the-fly (OTF) usability evaluation approach that integrates UI adaptation features and a user feedback mechanism into a mobile app. The developed solution enables us to continuously track various context information data and collect user feedback, e.g., whether The left screenshot in Fig.…”

Section: Usability Evaluationmentioning

confidence: 99%

Integrated model-driven development of self-adaptive user interfaces

et al. 2020

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Modern user interfaces (UIs) are increasingly expected to be plastic, in the sense that they retain a constant level of usability, even when subjected to context changes at runtime. Self-adaptive user interfaces (SAUIs) have been promoted as a solution for context variability due to their ability to automatically adapt to the context-of-use at runtime. The development of SAUIs is a challenging and complex task as additional aspects like context management and UI adaptation have to be covered. In classical model-driven UI development approaches, these aspects are not fully integrated and hence introduce additional complexity as they represent crosscutting concerns. In this paper, we present an integrated model-driven development approach where a classical model-driven development of UIs is coupled with a model-driven development of context-of-use and UI adaptation rules. We base our approach on the core UI modeling language IFML and introduce new modeling languages for contextof-use (ContextML) and UI adaptation rules (AdaptML). The generated UI code, based on the IFML model, is coupled with the context and adaptation services, generated from the ContextML and AdaptML model, respectively. The integration of the generated artifacts, namely UI code, context, and adaptation services in an overall rule-based execution environment, enables runtime UI adaptation. The benefit of our approach is demonstrated by two case studies, showing the development of SAUIs for different application scenarios and a usability study which has been conducted to analyze end-user satisfaction of SAUIs.

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