Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding How eHealth Coaches Tailor Support For Weight Loss: Towards the Design of Person-Centered Coaching Systems

eHealth coaching is a promising approach for delivering effective, person-centered, digital health interventions. Yet, the way eHealth coaches ‘tailor’ interventions using technology is currently unclear. This study aims to understand how eHealth coaches tailor support to clients seeking to lose weight. Nine in-depth interviews with eHealth coaches were conducted. The data were analyzed using thematic analysis, deriving three themes: ‘Understanding the client,’ ‘Adapting Support,’ and ‘Navigating challenges. ’ Coaches used both assessment tools and their growing relationship to understand client's needs. Coaches adapted the pace and nature of educational content, utilized their clients' preferred technologies, and adopted different personas to meet the needs of each individual. Providing tailored support also presented challenges for eHealth coaches, with technology eroding personal and professional boundaries. Our work interrogates what it means to ‘tailor’ support, through exploring coaching as a humanistic approach to behaviour change, with implications for the design of person-centered digital coaching interventions/systems.
Asynchronous Collaboration Systems for Evolving Information

I aim to understand how individuals may be motivated non-financially to collaborate in maintaining evolving information asynchronously. I have developed two systems, Drafty and Sketchy, that provide evidence of achieving this goal at differing timescales. In Drafty, unpaid contributors voluntarily update a tabular dataset of Computer Science professor profiles at a multi-year timescale. In Sketchy, university students simultaneously sketch and co-inspire each other during voluntary UI/UX sketching activities at a sub-hour timescale. I seek to integrate lessons from both systems to develop a theoretical framework that uses a feedback cycle to provide users with practical random examples from evolving information to motivate new contributions. I will further develop this theoretical framework by extending Drafty to generate insights to automatically attract visitors and create the Virtual Readability Lab (VRL) to help users improve their reading performance through personalized readability tests and deepen our understanding of information design human performance. Both systems will give users the agency to seek randomized insights within a naturalistic setting to motivate continuous contributions to each system’s evolving information.
How do Users Expect their Smart Memory Vaults to Utilize Their Shared Memories?

Social media (SM) is a popular accessible form of smart memory vaults. Prior work shows that users are still unable to understand how their shared memories are used by smart systems to create Smart Interactions involving Personal Memories (SIPMs). This can lead to negative social repercussions such as cyberbullying. This work investigates the most memorable SIPMs on Facebook for Egyptian users and their impact on platform usage. We conducted an online survey (N=53) requesting critical incident reports about surprising Facebook SIPMs. The most remembered SIPMs were: customizing advertisements, cuing offline interactions, sharing data with third parties and personalizing the newsfeed. Our results suggest that SIPMs, particularly customized advertisements act as ambient memory augmentation solutions to the users’ shared memories. Additionally, negative platform perception does not necessarily translate into reduction of platform usage. Our work inspires the discussion about users expectations towards ambient usage of their data on smart memory vaults.
PEACE: A Model of Key Social and Emotional Qualities of Conversational Chatbots

Open-domain chatbots engage with users in natural conversations to socialize and establish bonds. However, designing and developing an effective open-domain chatbot is challenging. It is unclear what qualities of a chatbot most correspond to users’ expectations and preferences. Even though existing work has considered a wide range of aspects, some key components are still missing. For example, the role of chatbots’ ability to communicate with humans at the emotional level remains an open subject of study. Furthermore, these trait qualities are likely to cover several dimensions. It is crucial to understand how the different qualities relate and interact with each other and what the core aspects would be. For this purpose, we first designed an exploratory user study aimed at gaining a basic understanding of the desired qualities of chatbots with a special focus on their emotional intelligence. Using the findings from the first study, we constructed a model of the desired traits by carefully selecting a set of features. With the help of a large-scale survey and structural equation modeling, we further validated the model using data collected from the survey. The final outcome is called the PEACE model (Politeness, Entertainment, Attentive Curiosity, and Empathy). By analyzing the dependencies between the different PEACE constructs, we shed light on the importance of and interplay between the chatbots’ qualities and the effect of users’ attitudes and concerns on their expectations of the technology. Not only PEACE defines the key ingredients of the social qualities of a chatbot, it also helped us derive a set of design implications useful for the development of socially adequate and emotionally aware open-domain chatbots.
Toolkits & Wearables: Developing Toolkits for Exploring Wearable Designs

Designing wearables is a complex task that includes many layers, such as wearability, interactivity, functionality, social and cultural considerations. For decades now, prototyping toolkits are proposed to aid diverse types of audiences in exploring the design of smart accessories and garments. However, the task of designing toolkits for wearables has not received a comprehensive discussion and systematic reflection. In this workshop, we look into challenges, opportunities, and lessons learned in using, developing and evaluating wearable toolkits by focusing on their target groups, purposes, effects on the final designs. By bringing together researchers and practitioners who are experienced with the design, use and assessment of wearable toolkits, we see a particular opportunity in providing a broader perspective on defining the future of wearable toolkit designs.
Perceiving Affordances Differently: The Unintended Consequences When Young Autistic Adults Engage with Social Media

Social media can facilitate numerous benefits, ranging from facilitating access to social, instrumental, financial, and other support, to professional development and civic participation. However, these benefits may not be generalizable to all users. Therefore, we conducted an ethnographic case study with eight Autistic young adults, ten staff members, and four parents to understand how Autistic users of social media engage with others, as well as any unintended consequences of use. We leveraged an affordances perspective to understand how Autistic young adults share and consume user-generated content, make connections, and engage in networked interactions with others via social media. We found that they often used a literal interpretation of digital affordances that sometimes led to negative consequences including physical harm, financial loss, social anxiety, feelings of exclusion, and inadvertently damaging their social relationships. We make recommendations for redesigning social media affordances to be more inclusive of neurodiverse users.
Barriers to Online Dementia Information and Mitigation

There is growing interest in HCI to study ways to support access to accurate, accessible, relevant online health information for different populations. Yet, there remains a need to understand the barriers that are posed by the way our platforms are designed as well as how we might overcome these barriers for people with dementia. To address this, we conducted sixteen interviews and observation sessions with people with mild to moderate dementia. Our analysis uncovered four barriers to online health information and corresponding mitigation strategies that participants employed. We discuss how HCI researchers may apply these findings towards new technical approaches and standards concerning information accessibility and credibility for neurodiverse populations. Finally, we broaden the scope of HCI research to include investigations of the accessibility and credibility of online information for people with age-related cognitive impairment independent of proxies.
“It's A Drag”: Exploring How to Improve Parents’ Experiences of Managing Mobile Device Use During Family Time

Research reveals that managing mobile device use during family time can be a source of stress for parents. In particular, it can create conflict in their relationships. As such, there is a need to understand how these problematic experiences might be addressed by new approaches to technology design. This paper presents a study in which 14 parents were prompted to reflect on how their experiences and relationships could be improved by four design proposals. These proposals resulted from ideation workshops involving 12 professional designers, and were presented as scenario-based storyboards during interviews. Our interviews revealed three design approaches that appealed to parents. We describe seven benefits that parents imagined these approaches would have, and discuss ways in which they should be further explored. Thus, we contribute to a more complete understanding of how technology design might better support parents’ aspirations for how devices are used within the family.
Meeting Users Where They Are: User-centered Design of an Automated Text Messaging Tool to Support the Mental Health of Young Adults

Young adults have high rates of mental health conditions, but most do not want or cannot access formal treatment. We therefore recruited young adults with depression or anxiety symptoms to co-design a digital tool for self-managing their mental health concerns. Through study activities—consisting of an online discussion group and a series of design workshops—participants highlighted the importance of easy-to-use digital tools that allow them to exercise independence in their self-management. They described ways that an automated messaging tool might benefit them by: facilitating experimentation with diverse concepts and experiences; allowing variable depth of engagement based on preferences, availability, and mood; and collecting feedback to personalize the tool. While participants wanted to feel supported by an automated tool, they cautioned against incorporating an overtly human-like motivational tone. We discuss ways to apply these findings to improve the design and dissemination of digital mental health tools for young adults.
AirRes Mask: A Precise and Robust Virtual Reality Breathing Interface Utilizing Breathing Resistance as Output Modality

Increased levels of interactivity and multi-sensory stimulation have been shown to enhance the immersion of Virtual Reality experiences. We present the AirRes mask that enables users to utilize their breathing for precise natural interactions with the virtual environment without suffering from limitations of the sensing equipment such as motion artifacts. Furthermore, the AirRes mask provides breathing resistance as novel output modality that can be adjusted in real-time by the application. In a user study, we demonstrate the mask’s precision measurements for interaction as well as its ability to use breathing resistance to communicate contextual information such as adverse environmental conditions that affect the user’s virtual avatar. Our results show that the AirRes mask enhances virtual experiences and has the potential to create more immersive scenarios for applications by enforcing the perception of danger or improving situational awareness in training simulations, or for psychotherapy by providing additional physical stimuli.
Critical Incident Technique and Gig-Economy Work (Deliveroo): Working with and Challenging Assumptions around Algorithms

Decision-making algorithms can be obscure and fast-moving. This is especially the case in the context of the algorithm that mediates the work of Deliveroo riders. Forming a critical part of the food delivery platform, the algorithm’s obscurity and shifting nature is a part of its design. In this paper we argue that adapting usability techniques like the Critical Incident Technique (CIT) may provide one way to better understand algorithms and platform work. Though there are many methods to understand algorithms like this, asking people about negative or positive interactions with them and what they think provoked them can produce fruitful avenues for HCI research into the impacts of platforms on gig-economy work. We argue that despite the results being an assumption, assumptions from the algorithmically managed are interesting materials to challenge the researchers’ own assumptions about their context, and to, therefore, better scope out contexts and iterate future research.
Prediction for Retrospection: Integrating Algorithmic Stress Prediction into Personal Informatics Systems for College Students’ Mental Health

Reflecting on stress-related data is critical in addressing one’s mental health. Personal Informatics (PI) systems augmented by algorithms and sensors have become popular ways to help users collect and reflect on data about stress. While prediction algorithms in the PI systems are mainly for diagnostic purposes, few studies examine how the explainability of algorithmic prediction can support user-driven self-insight. To this end, we developed MindScope, an algorithm-assisted stress management system that determines user stress levels and explains how the stress level was computed based on the user’s everyday activities captured by a smartphone. In a 25-day field study conducted with 36 college students, the prediction and explanation supported self-reflection, a process to re-establish preconceptions about stress by identifying stress patterns and recalling past stress levels and patterns that led to coping planning. We discuss the implications of exploiting prediction algorithms that facilitate user-driven retrospection in PI systems.
Forgetting Practices in the Data Sciences

HCI engages with data science through many topics and themes. Researchers have addressed biased dataset problems, arguing that bad data can cause innocent software to produce bad outcomes. But what if our software is not so innocent? What if the human decisions that shape our data-processing software, inadvertently contribute their own sources of bias? And what if our data-work technology causes us to forget those decisions and operations? Based in feminisms and critical computing, we analyze forgetting practices in data work practices. We describe diverse beneficial and harmful motivations for forgetting. We contribute: (1) a taxonomy of data silences in data work, which we use to analyze how data workers forget, erase, and unknow aspects of data; (2) a detailed analysis of forgetting practices in machine learning; and (3) an analytic vocabulary for future work in remembering, forgetting, and erasing in HCI and the data sciences.
Demonstrating Electrical Head Actuation: Enabling Interactive Systems to Directly Manipulate Head Orientation

We demonstrate a novel interface concept in which interactive systems directly manipulate the user's head orientation. We implement this using electrical-muscle-stimulation (EMS) of the neck muscles, which turns the head around its yaw (left/right) and pitch (up/down) axis. As the first exploration of EMS for head actuation, we characterized which muscles can be robustly actuated. Then, we demonstrated how it enables interactions not possible before by building a range of applications, such as (1) directly changing the user's head orientation to locate objects in AR; (2) a sound controller that uses neck movements as both input and output; (3) synchronizing head orientations of two users, which enables a user to communicate head nods to another user while listening to music; and (4) rendering force feedback from VR punches on the head by actuating the user's neck.
(Dis)Appearables: A Concept and Method for Actuated Tangible UIs to Appear and Disappear based on Stages

(Dis)Appearables is an approach for actuated Tangible User Interfaces (TUIs) to appear and disappear. This technique is supported by Stages: physical platforms inspired by theatrical stages. Self-propelled TUI’s autonomously move between front and back stage allowing them to dynamically appear and disappear from users’ attention. This platform opens up a novel interaction design space for expressive displays with dynamic physical affordances. We demonstrate and explore this approach based on a proof-of-concept implementation using two-wheeled robots, and multiple stage design examples. We have implemented a stage design pipeline which allows users to plan and design stages that are composed with front and back stages, and transition portals such as trap doors or lifts. The pipeline includes control of the robots, which guides them on and off stage. With this proof-of-concept prototype, we demonstrated a range of applications including interactive mobility simulation, self re-configuring desktops, remote hockey, and storytelling/gaming. Inspired by theatrical stage designs, this is a new take on ‘controlling the existence of matter’ for user experience design.
Detecting challenge from physiological signals: A primary study with a typical game scenario

Challenge is the core element of digital games. Game challenge with an appropriate type and level that matches with players’ skill, experience and motivation would lead players to achieve the optimal player experience. With a wider spectrum of challenge types such as physical, cognitive, and emotional challenge provided by modern digital games, a questionnaire tool of CORGIS has recently been developed to evaluate the whole range of challenge experiences subjectively. However, such challenge experiences still lack measures to evaluate them objectively ”in real time”. To explore the possibility to detect different challenge types based on physiological signals, we conducted an experiment where 12 players’ physiological signals (EDA, ECG, EMG, RSP and TEM) of overcoming different types of game challenges were recorded. With 80 extracted physiological features, two methods (ANOVA-based and Regression-based) were adopted to select challenge-related physiological features. Results of logistic regression models showed that both methods obtained detection accuracy over 60%, which suggest potential for further development of a real-time challenge measurement instrument.
Assisting Teaching Assistants with Automatic Code Corrections

Undergraduate Teaching Assistants(TAs) in Computer Science courses are often the first and only point of contact when a student gets stuck on a programming problem. But these TAs are often relative beginners themselves, both in programming and in teaching. In this paper, we examine the impact of availability of corrected code on TAs’ ability to find, fix, and address bugs in student code. We found that seeing a corrected version of the student code helps TAs debug code 29% faster, and write more accurate and complete student-facing explanations of the bugs (30% more likely to correctly address a given bug). We also observed that TAs do not generally struggle with the conceptual understanding of the underlying material. Rather, their difficulties seem more related to issues with working memory, attention, and overall high cognitive load.
Sad or just jealous? Using Experience Sampling to Understand and Detect Negative Affective Experiences on Instagram

Social Network Services (SNSs) evoke diverse affective experiences. While most are positive, many authors have documented both the negative emotions that can result from browsing SNS and their impact: Facebook depression is a common term for the more severe results. However, while the importance of the emotions experienced on SNSs is clear, methods to catalog them, and systems to detect them, are less well developed. Accordingly, this paper reports on two studies using a novel contextually triggered Experience Sampling Method to log surveys immediately after using Instagram, a popular image-based SNS, thus minimizing recall biases. The first study improves our understanding of the emotions experienced while using SNSs. It suggests that common negative experiences relate to appearance comparison and envy. The second study captures smartphone sensor data during Instagram sessions to detect these two emotions, ultimately achieving peak accuracies of 95.78% (binary appearance comparison) and 93.95% (binary envy).
How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions

Machine learning tools have been deployed in various contexts to support human decision-making, in the hope that human-algorithm collaboration can improve decision quality. However, the question of whether such collaborations reduce or exacerbate biases in decision-making remains underexplored. In this work, we conducted a mixed-methods study, analyzing child welfare call screen workers’ decision-making over a span of four years, and interviewing them on how they incorporate algorithmic predictions into their decision-making process. Our data analysis shows that, compared to the algorithm alone, workers reduced the disparity in screen-in rate between Black and white children from 20% to 9%. Our qualitative data show that workers achieved this by making holistic risk assessments and adjusting for the algorithm’s limitations. Our analyses also show more nuanced results about how human-algorithm collaboration affects prediction accuracy, and how to measure these effects. These results shed light on potential mechanisms for improving human-algorithm collaboration in high-risk decision-making contexts.
Spirituality at the Breakfast Table: Experiences of Christian Online Worship Services

Since the COVID-19 pandemics, we have witnessed an increase in online worship services. Nevertheless, HCI has little insight into how technological mediation influences religious experiences and how technology should be designed for use in religious contexts. Therefore, we see a unique opportunity to understand better real-world experiences of technology use in religious rituals and, more specifically, in online worship services. Inspired by contextual design, We virtually observed and interviewed eight persons during and after participation in online worship services. We identified a field of tension between faith, everyday life, individuality, and community. The data suggests that current online worship service systems do not account for believers’ needs for community, faith, or extraordinariness. We discuss opportunities for future research and design, and aim to contribute to the understanding of online worship service experiences and the design of technology-mediated religious experiences.
From Sensable to Sensible Spaces: Enhancing the Sensibility of a Home Office using Stress-Aware Deep Reinforcement Learning in Virtual Environments

Being confronted with a dulling perception and challenges to be creative when working at home, I imagined my home office to sense my state of arousal and search together with me for inspirational moments by infusing its familiar appearance with distortion. In two prototypical human-home office interaction systems, we use Virtual Reality (VR), Deep Reinforcement Learning (DRL) and Galvanic Skin Response (GSR) to enhance a home office’s sensibility towards its user’s level of arousal as well as to enlarge its textural action space. Although physiological feedback in machine learning faces low learning rates, the resulting interaction offers a fresh perspective on our human-home office relation.