USER MODELLING AND ADAPTIVE INTERACTION ON INTERACTIVE DASHBOARDS

Abstract

Interactive information dashboards are data visualisation tools that enable interaction with complex underlying datasets using visualisations such as charts and maps typically on a single display. The popularity of dashboards has grown across key sectors such as healthcare, education and energy, driven by the abundance of available data. Still, users face various challenges when interacting with dashboards, ranging from insufficient support for essential functionalities such as data-detail adjustment to problems with data presentation such as information overload. These problems subject users to high cognitive demands, complicate information retrieval and increase the risk of arriving at incorrect conclusions, ultimately leading to erroneous decision-making. Dashboard issues are sometimes due to developers prioritising aesthetics over functionality. At other times, they arise from a mismatch between users' visual literacy level expected by dashboard developers and the actual level of the users. When dashboard users encounter interaction problems, they exhibit certain interaction strategies as workarounds to overcome the problems. Modelling user behaviour on dashboards can shed light on these workarounds especially when applied in problematic situations. Strategies employed by users in response to interaction problems have, to a large extent, not been thoroughly explored. This thesis addresses this gap by identifying the interaction and information presentation problems faced by dashboard users, adaptation techniques that could address these problems and user strategies applied in response to problems. Results of a literature review and an interview study highlighted various problems faced by users, and at times, a disconnect between problems, adaptations and strategies. Subsequently, an experiment was conducted to identify user strategies indicative of problems when encountering four established interaction and information presentation problems: information overload, inappropriate data order \& grouping, ineffective data presentation and misaligned visual literacy expectations. These problems were prioritised based on their severity and the limited understanding of user strategies when encountering them. We found clear distinctions between the strategies applied on problematic and adapted dashboards. Then, we incorporated the strategies, along with graph literacy, in user models to predict usability. In a final user study, we ecologically validated the effect of the majority of the influential user strategies on usability in real-world dashboards. While filtering data was linked to negative outcomes, customisation made users more effective. Encouragingly, usability predictions were more accurate on problematic dashboards and challenging tasks. These promising results open up avenues for tailored interventions to address the problems in real time.