The Effects of Customisation on the Usability of Visual Analytics Dashboards: the Good, the Bad, and the UglyVisual 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 five 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 that predict the usability of dashboards-in-use and associated adaptive interaction techniques to assist users.2025HAHatim Alsayahani et al.Interactive Data VisualizationVisualization Perception & CognitionIUI
Effects of Alternative Scatterplot Designs on BeliefViewers tend to underestimate correlation in positively correlated scatterplots. However, systematically changing the size and opacity of scatterplot points can bias estimates upwards, correcting for this underestimation. Here, we examine whether the application of these visualisation techniques goes beyond a simple perceptual effect and could actually influence beliefs about information from trusted news sources. We present a fully-reproducible study in which we demonstrate that scatterplot manipulations that are able to correct for the correlation underestimation bias can also induce stronger levels of belief change compared to conventional scatterplots presenting identical data. Consequently, we show that novel visualisation techniques can be used to drive belief change, and suggest future directions for extending this work with regards to altering attitudes and behaviours.2025GSGabriel Strain et al.University of Manchester, Department of Computer Science, Faculty of Science and EngineeringInteractive Data VisualizationVisualization Perception & CognitionCHI
Queer Joy on Social Media: Exploring the Expression and Facilitation of Queer Joy in Online PlatformsQueer Joy is conceptualised as a form of resistance to oppression by celebrating queerness in the face of adversity. This research aimed to centre queer joy and understand how it is expressed and may be facilitated in online spaces. To do this we conducted a survey with 100 UK participants who indicated they identified as LGBTQ+ on the online recruitment platform Prolific. We asked a series of open and closed questions in an online survey to investigate 1) what queer joy looks like on social media 2) how queer joy content is engaged with on social media 3) which platforms are perceived to facilitate queer joy and 4) how queer people protect their privacy online. The results suggested that to facilitate queer joy online, platforms should allow flexible self expression and community engagement, while allowing for granular control over privacy and the audience such content is shown to.2025MSMadeleine Steeds et al.University College Dublin, School of Information and Communication StudiesSocial Platform Design & User BehaviorGender & Race Issues in HCILGBTQ+ Community Technology DesignCHI
Pixel Memories: Do Lifelog Summaries Fail to Enhance Memory but Offer Privacy-Aware Memory Assessments?We explore the metaphorical "daily memory pill" concept – a brief pictorial lifelog recap aimed at reviving and preserving memories. Leveraging psychological strategies, we explore the potential of such summaries to boost autobiographical memory. We developed an automated lifelogging memory prosthesis and a research protocol (Automated Memory Validation ``AMV'') for conducting privacy-aware, in-situ evaluations. We conducted a real-world lifelogging experiment for a month (n=11). We also designed a browser ``Pixel Memories’’ for browsing one-week worth of lifelogs. The results suggest that daily timelapse summaries, while not yielding significant memory augmentation effects, also do not lead to memory degradation. Participants' confidence in recalled content remains unaltered, but the study highlights the challenge of users' overestimation of memory accuracy. Our core contributions, the AMV protocol and "Pixel Memories" browser, advance our understanding of memory augmentations and offer a privacy-preserving method for evaluating future ubicomp systems.2025PEPassant ElAgroudy et al.German Research Centre for Artificial Intelligence (DFKI); RPTU KaiserslauternContext-Aware ComputingUbiquitous ComputingCHI
Mnemosyne - Supporting Reminiscence for Individuals with Dementia in Residential Care SettingsReminiscence is known to play an important part in helping to mitigate the effects of dementia. Within the HCI community, work has typically focused on supporting reminiscence at an individual or social level but less attention has been given to supporting reminiscence in residential care settings. This lack of research became particularly apparent during the COVID pandemic when traditional forms of reminiscence involving physical artefacts and face-to-face interactions became especially challenging. In this paper we report on the design, development and evaluation of a reminiscence system, deployed in a residential care home over a two-year-period that included the pandemic. Mnemosyne comprises a pervasive display network and a browser-based application whose adoption and use we explored using a mixed methods approach. Our findings offer insights that will help shape the development and evaluation of future systems, particularly those that use pervasive displays to support unsupervised reminiscence.2024ABAndrea Baumann et al.Lancaster UniversityElderly Care & Dementia SupportCHI
Effects of Point Size and Opacity Adjustments in ScatterplotsSystematically changing the size and opacity of points on scatterplots can be used to induce more accurate perceptions of correlation by viewers. Evidence points to the mechanisms behind these effects being similar, so one may expect their combination to be additive regarding their effects on correlation estimation. We present a fully-reproducible study in which we combine techniques for influencing correlation perception to show that in reality, effects of changing point size and opacity interact in a non-additive fashion. We show that there is a great deal of scope for using visual features to change viewers’ perceptions of data visualizations. Additionally, we use our results to further interrogate the perceptual mechanisms at play when changing point size and opacity in scatterplots.2024GSGabriel Strain et al.University of ManchesterInteractive Data VisualizationVisualization Perception & CognitionCHI
FARPLS: A Feature-Augmented Robot Trajectory Preference Labeling System to Assist Human Labelers’ Preference ElicitationPreference-based learning aims to align robot task objectives with human values. One of the most common methods to infer human preferences is by pairwise comparisons of robot task trajectories. Traditional comparison-based preference labeling systems seldom support labelers to digest and identify critical differences between complex trajectories recorded in videos. Our formative study (N = 12) suggests that individuals may overlook non-salient task features and establish biased preference criteria during their preference elicitation process because of partial observations. In addition, they may experience mental fatigue when given many pairs to compare, causing their label quality to deteriorate. To mitigate these issues, we propose FARPLS, a Feature-Augmented Robot trajectory Preference Labeling System. FARPLS highlights potential outliers in a wide variety of task features that matter to humans and extracts the corresponding video keyframes for easy review and comparison. It also dynamically adjusts the labeling order according to users’ familiarities, difficulties of the trajectory pair, and level of disagreements. At the same time, the system monitors labelers’ consistency and provides feedback on labeling progress to keep labelers engaged. . A between-subjects study (N = 42, 105 pairs of robot pick-and-place trajectories per person) shows that FARPLS can help users establish preference criteria more easily and notice more relevant details in the presented trajectories than the conventional interface. FARPLS also improves labeling consistency and engagement, mitigating challenges in preference elicitation without raising cognitive loads significantly.2024HLHanfang Lyu et al.Human-Robot Collaboration (HRC)Prototyping & User TestingIUI
Trade-offs in Sampling and Search for Early-stage Interactive Machine LearningFor many automated classification tasks, collecting labeled data is the key barrier to training a useful supervised model. Interfaces for interactive labeling tighten the loop of labeled data collection and model development, enabling a subject-matter expert to quickly establish the feasibility of a classifier for addressing a problem of interest. These interactive machine learning (IML) interfaces iteratively sample unlabeled data for annotation, train a new model, and display feedback on the model's estimated performance. Different sampling strategies affect both the rate at which the model improves and the bias of performance estimates. We compare the performance of three sampling strategies in the "early-stage" of label collection, starting from zero labeled data. By simulating a user's interactions with an IML labeling interface, we demonstrate a trade-off between improving a text classifier's performance and computing unbiased estimates of that performance. We show that supplementing early-stage sampling with user-guided text search can effectively "seed" a classifier with positive documents without compromising generalization performance—particularly for imbalanced tasks where positive documents are rare. We argue for the benefits of incorporating search alongside active learning in IML interfaces and identify design trade-offs around the use of non-random sampling strategies.2022ZLZachary Levonian et al.Human-LLM CollaborationComputational Methods in HCIIUI
The Role of Uncertainty as a Facilitator to Reflection in Self-TrackingSelf-trackers reflect on their personal data to understand their behaviour and plan accordingly. Often, this reflection involves uncertainty, which can affect decision-making. To better understand the role of uncertainty, we conducted an interview study to comprehend how uncertainty influences reflection and the resulting actions. Our findings suggest that, in addition to the conventional role of uncertainty as a barrier, uncertainty also manifests as a trigger and facilitator to reflection. We discuss functionalities to alleviate the negative effects of uncertainty (e.g. incorporating users' expectations in self-tracking), and leverage its positive effects through activity reconstruction mechanisms.2020DADeemah Alqahtani et al.Uncertainty VisualizationVisualization Perception & CognitionDIS
Do humans imitate robots? An investigation of strategic social learning in Human-Robot Interaction.Theories on social learning indicate that imitative choices are usually performed whenever copying the others’ behaviour has no additional cost. Here, we extended such investigations of social learning to Human-Robot Interaction (HRI). Participants played the Economic Investment Game with a robot banker while observing another robot player also investing in the robot banker. By manipulating the robot banker payoff, three conditions of unfairness were created: (1) unfair payoff for the participants, (2) unfair payoff for the robot player and (3) unfair payoff for both. Results showed that when the payoff was low for the participants and high for the robot player, participants invested more money in the robot banker than when both parties received a low return. Also, for this specific condition, participants’ investments increased further with a more interactive robot player (defined as demonstrating increased attention, congruent movements and speech) This suggests that social and cognitive human competencies can be used and transposed to non-human agents. Further, imitation can potentially be extended to HRI, with interactivity likely having a key role in increasing this effect.2020DZDebora Zanatto et al.Human Pose & Activity RecognitionSocial Robot InteractionHuman-Robot Collaboration (HRC)HRI
Supporting Stimulation Needs in Dementia Care through Wall-Sized DisplaysBeside reminiscing, the increasing cognitive decline in dementia can also be addressed through sensory stimulation allowing the immediate, nonverbal engagement with the world through one's senses. Much HCI work has prioritized cognitive stimulation for reminiscing or personhood often on small screens, while less research has explored sensory stimulation like the one enabled by large displays. We describe a year-long deployment in a residential care home of a wall-sized display, and explored its domestication through 24 contextual interviews. Findings indicate strong engagement and attachment to the display which has inspired four psychosocial interventions using online generic content. We discuss the value of these findings for personhood through residents' exercise of choices, the tension between generic/personal content and its public/private use, the importance of participatory research approach to domestication, and the infrastructure-based prototype, illustrated by the DementiaWall and its generative quality.2020CSCorina Sas et al.Lancaster UniversityMixed Reality WorkspacesElderly Care & Dementia SupportPrototyping & User TestingCHI
Shaping the Design of Smartphone-Based Interventions for Self-HarmSelf-harm is a prevalent issue amongst young people, yet it is thought around 40% will never seek professional help due to stigma surrounding it. It is generally a way of coping with emotional distress and can have a range of triggers which are highly heterogeneous to the individual. In a move towards enhancing the accessibility of personalized interventions for self-harm, we undertook a three-stage study. We first conducted interviews with 4 counsellors in self-harm to understand how they clinically respond to self-harm triggers. We then ran a survey with 37 young people, to explore perceptions of mobile sensing, and current and future uses for smartphone-based interventions. Finally, we ran a workshop with 11 young people to further explore how a context-aware self-management application might be used to support them. We contribute an in-depth understanding of how triggers for self-harm might be identified and subsequently predicted and prevented using mobile-sensing technology.2020MHMahsa Honary et al.University of CambridgeMental Health Apps & Online Support CommunitiesContext-Aware ComputingCHI
Should I Agree? Delegating Consent Decisions Beyond the IndividualObtaining meaningful user consent is increasingly problematic in a world of numerous, heterogeneous digital services. Current approaches (e.g. agreeing to Terms and Conditions) are rooted in the idea of individual control despite growing evidence that users do not (or cannot) exercise such control in informed ways. We consider an alternative approach whereby users can opt to delegate consent decisions to an ecosystem of third-parties including friends, experts, groups and AI entities. We present the results of a study that used a technology probe at a large festival to explore initial public responses to this reframing -- focusing on when and to whom users would delegate such decisions. The results reveal substantial public interest in delegating consent and identify differing preferences depending on the privacy context, highlighting the need for alternative decision mechanisms beyond the current focus on individual choice.2019BNBettina Nissen et al.University of EdinburghPrivacy by Design & User ControlPrivacy Perception & Decision-MakingCHI
Vajra: Step-by-step programming with natural languageBuilding natural language programming systems that are geared towards end-users requires the abstraction of formalisms inherently introduced by programming languages, capturing the intent of natural language inputs and mapping it to existing programming language constructs. We present a novel end-user programming paradigm for Python, which maps natural language commands into Python code. The proposed semantic parsing model aims to reduce the barriers for producing well-formed code (syntactic gap) and for exploring third-party APIs (lexico-semantic gap). The proposed method was imple-mented in a supporting system and evaluated in a usability study involving programmers as well as non-programmers. The results show that both groups are able to produce code with or without prior programming experience.2019VSViktor Schlegel et al.Voice User Interface (VUI) DesignHuman-LLM CollaborationIUI
Evaluating the Impact of Pseudo-Colour and Coordinate System on the Detection of Medication-induced ECG ChangesThe electrocardiogram (ECG), a graphical representation of the heart's electrical activity, is used for detecting cardiac pathologies. Certain medications can produce a complication known as 'long QT syndrome', shown on the ECG as an increased gap between two parts of the waveform. Self-monitoring for this could be lifesaving, as the syndrome can result in sudden death, but detecting it on the ECG is difficult. Here we evaluate whether using pseudo-colour to highlight wave length and changing the coordinate system can support lay people in identifying increases in the QT interval. The results show that introducing colour significantly improves accuracy, and that whilst it is easier to detect a difference without colour with Cartesian coordinates, the greatest accuracy is achieved when Polar coordinates are combined with colour. The results show that applying simple visualisation techniques has the potential to improve ECG interpretation accuracy, and support people in monitoring their own ECG.2019AAAlaa Alahmadi et al.University of ManchesterVisualization Perception & CognitionMedical & Scientific Data VisualizationCHI
Mediating Color Filter Exploration with Color Theme Semantics Derived from Social Curation DataDespite the popularity of photo editors used to improve image attractiveness and expressiveness on social media, many users have trouble making sense of color filter effects and locating a preferred filter among a set of designer-crafted candidates. The problem gets worse when more computer-generated filters are introduced. To enhance filter findability, we semantically name and organize color effects leveraging data curated by creative communities online. We first model semantic mappings between color themes and keywords in everyday language. Next, we index and organize each filter by the derived semantic information. We conduct three separate studies to investigate the benefit of the semantic features on filter exploration. Our results indicate that color theme semantics constructed through social curation enhances filter findability, providing important implications into how to use the wisdom of the crowd to improve user experience with image editors.2018ZWZiming Wu et al.Language and LinguisticsCSCW
Back to Analogue: Self-Reporting for Parkinson's DiseaseWe report the process used to create artefacts for self-reporting Parkinson's Disease symptoms. Our premise was that a technology-based approach would provide participants with an effective, flexible, and resilient technique. After testing four prototypes using Bluetooth, NFC, and a microcontroller we accomplished almost full compliance and high acceptance using a paper diary to track day-to-day fluctuations over 49 days. This diary is tailored to each patient's condition, does not require any handwriting, allows for implicit reminders, provides recording flexibility, and its answers can be encoded automatically. We share five design implications for future Parkinson's self-reporting artefacts: reduce participant completion demand, design to offset the effect of tremor on input, enable implicit reminders, design for positive and negative consequences of increased awareness of symptoms, and consider the effects of handwritten notes in compliance, encoding burden, and data quality.2018JVJulio Vega et al.University of ManchesterMotor Impairment Assistive Input TechnologiesCHI
Intersectionality as a Lens to Promote Equity and Inclusivity within SIGCHIThe ACM SIGCHI community has been at the forefront of addressing issues of equity and inclusivity in the design and use of technology, accounting for various aspects of users’ identities such as gender, ethnicity, and sexuality. With this panel, we wish to explore how we, as SIGCHI, might better target similar goals of equity and inclusivity—across intersections—within our own community. We wish to create a forum for recognizing best practices regarding equity and inclusivity in participants’ local and global contexts that we might feasibly integrate across SIGCHI. By equally prioritizing the voices of those in the audience and on the panel, we intend to foster a lively and constructive discussion that will help us chart a way forward. The takeaways from this panel will be articulated into an article for the Interactions magazine, targeting the larger human-computer interaction (HCI) community.2018PWPamela J Wisniewski et al.The University of Central FloridaGender & Race Issues in HCIEmpowerment of Marginalized GroupsTechnology Ethics & Critical HCICHI