Visual analytics dashboards have become key tools for decision-making. Yet, information overload and high expectations of the actual graph literacy of users hinder their effective use. Customisation features such as global filters (filters that update all views), visualisation hiding, and graph switching have been proposed to improve dashboard usability and accommodate user diversity. However, time constraints and a lack of awareness of the available customisation functionalities are known to be barriers to engaging with customisations. In this paper, we examine how visual analytics dashboard customisation affects usability and identify existing risks and barriers. We found that short-term surface customisations improve the usability of dashboards, both objectively (users are quicker and more correct) and subjectively (users report a lower cognitive load). For this to happen, the engagement with customisations must not be exploratory but meaningful (i.e., longer than 5 seconds). We also identified a set of customisations that, if not implemented carefully, are detrimental to cognitive load and completion times. Informed by our findings, we provide insights for building user models to capture the usability of dashboards-in-use and associated adaptive interaction techniques to assist users when needed.

Visual analytics dashboards have become essential tools for decisionmaking.However, information overload and mismatches between designers' expected graph literacy and users' actual graph literacy can limit their effectiveness.Customisation has been proposed to mitigate these challenges and accommodate diverse user needs.Yet, customising dashboards is often time-consuming; users may not be aware of existing customisation features, or they may not have sufficient technical skills to use them.In this paper, we conduct an experiment (N=50) to examine if we can use nudging techniques to promote short-term surface customisations while not sacrificing the usability of interactive visual analytics dashboards.We found that while nudges do not necessarily increase the use of customisation functionalities, they benefit usability.Specifically, the Social Comparisons nudge supports decision-making, while the Just-in-Time Prompts nudge reduces task completion time.Our findings suggest that nudges should be tailored to graph literacy as users with moderate graph literacy can benefit the most from nudges.

Information presentation problems on interactive dashboards are known to hinder decision-making. Since a traditional user-centred approach to designing usable dashboards cannot fully satisfy user demands, needs and skills, we isolate behavioural indicators of usability when users conduct typical information-seeking and comparison tasks. In a first study (N = 50), we identified strategies derived from 486,435 interaction events logged in a controlled setting with synthetic dashboards. User models consisting of these user strategies and graph literacy produced strong signals indicating that usability was predictable. In a second study (N = 65), we tested the initial insights on real-world dashboards. While most of our hypotheses were confirmed, graph literacy emerged as the best predictor of usability. Usability was better predicted in dashboards with problems, suggesting promising opportunities for automated usability evaluation and real-time support for users struggling with visual analytics dashboards.

Websites are frequently used by programmers to support the development process. This paper investigates programmer-Web interactions when coding, and combines observations of behaviour with assessments of the resulting source code. We report on an online observational study with ten undergraduate student programmers as they engaged in programming tasks of varying complexity. Screens were recorded of participants’ activities, and each participated in an interview. Videos and interviews were thematically analysed. Novice programmers employed various strategies for seeking and utilising online knowledge. The resulting source code was examined to determine the extent to which it met requirements and whether it contained errors. The source code analysis revealed that coding with the websites involved more coding time and effort, but increased the possibility of producing correct code. However, coding with websites also introduced instances of either incorrect or non-executable source code.

Interacting with and making sense of information dashboards is often problematic. Typically, users develop strategies to go around and overcome these problems. These strategies can be conceived as behavioural markers of cognitive processes that indicate problematic interactions. Consequently, if we were able to computationally model these strategies, we could detect if users are encountering problems in real time (and act accordingly). We conducted an experiment (N=63) to identify the interaction strategies users employ on problematic dashboards. We found that while existing challenges impact significantly on user performance, interventions to mitigate such challenges were especially beneficial for those with lower graph literacy. We identified the strategies employed by users when encountering problems: extensive page exploration as a reaction to information overload and use of customisation functionalities when understanding data is problematic. We also found that some strategies are indicators of performance in terms of task completion time and effectiveness: extensive exploration strategies were indicators of lower performance, while the exhibition of customisation strategies is associated with higher effectiveness.

Interactive dashboards enable viewing and interacting with complex underlying data using visualisations such as charts, tables, maps, or even text typically on a single display. By bringing the most important information in a single place, dashboards enable performance monitoring and support decision making. Although nowadays dashboards are widely adopted in many domains, they involve challenges that prevent users from utilising them as they were intended. For example, having a dashboard with too much data can negatively affect decision making and lead to misleading interpretation. Through this research, we identify and investigate the challenges associated with dashboards, what users do in response to those challenges, and what adaptations can be applied to mitigate these challenges. Consequently, we aim to examine and evaluate a set of adaptation techniques that can improve the experience of users interacting with dashboards.