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Program

All types of presentations occur concurrently at 3:00 – 4:00 PM.


Posters

P1: Co-designing a tailored self-management app with older adults with cancer and multimorbidities


Francis Kobekyaa, RN, PhD student, School of Nursing, University of British Columbia
Sien Sang-Wha, Msc, PhD(c), Computer Science Department, University of British Columbia
Margaret Thompson, School of Nursing, University of British Columbia
Hedges Penelope, School of Nursing, University of British Columbia
Martine Puts, RN, PhD, Lawrence S. Bloomberg Faculty of Nursing, University of Toronto
Leanne Currie, RN, PhD, School of Nursing, University of British Columbia
Kristen Haase, RN, PhD, School of Nursing, University of British Columbia

Abstract:
Introduction: Worldwide, older adults aged 60+ are disproportionately affected by cancer. In Canada, 2 in every 5 older adults aged ≥70 years is diagnosed with cancer with 70% living with other illnesses. Cancer self-management interventions tailored to the needs and abilities of older adults can optimize symptom management. However, few interventions target older adults with cancer and multimorbidities that provide digital self-management supports. Many apps for older adults have not been co-designed with older adults to increase usability and uptake. The purpose of this study was to co-design a self-management App to support older adults living with cancer and multimorbidities.

Methods: We used a user-centred design thinking approach, and older adults (median age-71) with lived experience to design a medium-fidelity prototype. Older adults with cancer and caregivers were recruited through community organizations and support groups to participate in co-design and evaluation of the app. Data has been iteratively integrated into the design process and analyzed descriptively and thematically.

Results: 20 older adults with cancer and caregivers participated in the design of the low-fidelity prototype and iteratively evaluated the medium-fidelity prototype. Participants with varying levels of technical know-how collectively emphasized the importance of tracking functions to make sense of information across physical and psychosocial symptoms linked to their health needs; clear visualizations; and organization of notes and reminders to communicate with care providers.

Conclusions: This App supports the complex healthcare needs of older adults with cancer creating a ‘home base’ for symptom management. Findings from this study will position the researchers to conduct feasibility testing and real-world implementation.

P2: Examining the influence of daily stress characteristics on subjective sleep quality and duration: A machine learning approach


Jin H. Wen, MA, Department of Psychology, University of British Columbia
Nancy L. Sin, PhD, Department of Psychology, University of British Columbia

Abstract:
Background: It is well-established people with inadequate sleep tend to have higher levels of stress. Much of the prior work has focused on perceived stress or exposure to major or minor stressors, but less research has focused on the links between sleep and stress appraisals, coping strategies, and coping efficacy. Exploratory approaches using fine-grained assessments may be warranted to examine which aspects of daily stress and coping are most related to sleep quality. Thus, the current study used machine learning methods to examine individual differences in daytime stress experiences (e.g., stressor types, appraisals, coping strategies and efficacy) and subjective sleep quality.

Methods: The analytical sample consisted of 245 participants from across British Columbia, Canada (ages 24-to-87; 72% women; 30% racial minorities), who were instructed to completed five mobile surveys over 14 days. Every morning, participants reported on their prior-night sleep. Every evening, they were asked about the day’s stressor types (e.g., arguments, home/family stressors, discrimination), appraisals, coping strategies, and perceived coping efficacy. We used random forest methods with grouped resampling to predict subjective sleep quality and duration from 26 aspects of daily stressful experiences. Variable importance metrics were used to rank the predictive abilities of these stress measures.

Results: Of the 26 daily stress and coping predictors, rumination, perceived stressfulness, coping, and perceived control were found to be the most important variables in predicting subsequent sleep quality and duration.

Conclusion: The findings expand upon prior literature by demonstrating the importance of specific aspects of daily stress and coping on sleep quality. Future work could further explore these associations by using intervention approaches to test whether targeting these stress experiences might promote better sleep quality or vice versa.

P3: Human-centred TV Videos – Collaborative Research and Design, with a Focus on Assisting People in Care Settings Who Have Dementia


Karen Lok Yi Wong, PhD Student, IDEA Lab, University of British Columbia
Lily Wong, family partner, IDEA Lab, University of British Columbia
Colin Leigh, patient partner, IDEA Lab, University of British Columbia
Annette Berndt, family partner, IDEA Lab, University of British Columbia
Lillian Hung, Assistant Professor, IDEA Lab, University of British Columbia

Abstract:
Background and Aim: TV videos have been found to enhance the well-being of people with moderate to severe dementia in care settings. They are relatively low-cost and familiar to most people. Over the last two years, our team has been co-designing TV videos tailored for people with moderate to severe dementia, co-implementing them with this population in hospital and long-term care settings, and co-evaluating their effectiveness. We aim to enhance the benefits of this technology for people with dementia.
Methods: Kitwood’s person-centred care model has guided our co-design, co-implementation, and co-evaluation processes. Our team comprises ten patient and family partners (i.e., people with lived experiences with dementia), 36 healthcare provider partners, three industry partners, one researcher and five trainees. Coming from diverse backgrounds helps us contribute knowledge and skills to the project. All team members are involved in all stages. We have regular weekly study site visits and monthly dialogues.
Findings and Outputs: We co-designed a series of TV videos (e.g., videos celebrating cultural festivals and supporting the daily activities of people with dementia from diverse cultural and language communities.) We co-implemented TV videos by reviewing the challenges healthcare providers face when implementing TV videos and coming up with solutions to facilitate the adoption of TV videos. We co-evaluated the effectiveness of the videos through dialogues with people with dementia who watched the TV videos, their family members, and healthcare providers. Our study is iterative. We identify gaps in the process and adjust accordingly to meet users’ needs. We work closely with users, as well as understand deeply about their contexts.
Implications: We hope our ongoing collaborative work can provide insights into the co-design, co-implementation, and co-evaluation of more TV videos tailored for people with moderate to severe dementia, especially those in care setting.

P4: Predicting Co-occurring Emotions in MetaTutor when Combining Eye-Tracking and Interaction Data from Separate User Studies


Rohit Murali, PhD, Computer Science, University of British Columbia

Abstract:
Learning can be improved by providing personalized feedback adapting to the emotions that the learner may be experiencing. There is initial evidence that co-occurring emotions can be predicted during learning in Intelligent Tutoring Systems (ITS) through eye-tracking and interaction data. Predicting co-occurring emotions is a complex task and merging datasets has the potential to improve predictive performance. In this paper, we combine data from two user studies with an ITS, and analyze whether there is an improvement in predictive performance of co-occurring emotions, despite the user studies using different eye-trackers. In the pursuit towards developing real affect-aware ITS, we look at whether we can isolate classifiers that perform better than a baseline. In this regard we perform a series of statistical analyses and test out the predictive performance of standard machine learning models as well as an ensemble classifier for the task of predicting co-occurring emotions.

P5: ChatrEx: Designing Explainable Chatbot Interfaces for Enhancing Usefulness, Transparency, and Trust


Anjali Khurana, PhD Student, School of Computing Science, Simon Fraser University
Parsa Alamzadeh, School of Computing Science, Simon Fraser University
Parmit K. Chilana, Associate Professor, School of Computing Science, Simon Fraser University

Abstract:
Recent progress in Artificial Intelligence (AI) systems such as task-oriented chatbots offer several opportunities to automate various tasks and support the use of complex application features. But, when breakdowns occur during a human-chatbot conversation, the lack of transparency and the “black-box” nature of task-oriented chatbots can make it difficult for end users to understand what went wrong and why. Recent HCI research on explainable AI (XAI) solutions recognizes the need to incorporate explainability features for improving transparency and trust. Inspired by recent HCI research on explainable AI solutions, we explored the design of in-application explainable chatbot interfaces (ChatrEx) that explain the underlying working of a chatbot during a breakdown. ChatrEx-VINC provides visual example-based step-by-step ex- planations in-context of the chat window whereas ChatrEx-VST provides explanations as a visual tour overlaid on the application interface. We implemented these chatbots for complex spread- sheet tasks and our comparative observational study (N=14) showed that the explanations provided by both ChatrEx-VINC and ChatrEx-VST enhanced users’ understanding of the reasons for a breakdown and improved users’ perceptions of usefulness, transparency, and trust. We identify several opportunities for future research to exploit explainable chatbot interfaces and better support human-chatbot interaction.

P6: Reading Apps in the School and Home: How Parents and Children Perceive eBooks Differently


Ariel Lee, MLIS, iSchool, University of British Columbia
Eric Meyers, faculty supervisor, iSchool, University of British Columbia

Abstract:
Digital reading apps, such as Epic or Razkids, combine collections of online texts with tools that encourage different forms of literary practice. Targeted primarily to early grades (K to 3), these apps typically gamify the reading experience, offering teachers, librarians, and parents an engaging mode to deliver instruction to children through a fun experience.

This poster presents data from a preliminary University of British Columbia research study investigating children’s use of reading apps and how it contributes to their literacy practice at school and home. Interviews were conducted with parent-child dyads and examined differences between parent and children perspectives on the importance and value of reading, preferences between print and ebooks, and parental understanding of data collection/information privacy in reading apps. All three participating groups were bilingual families, which revealed differences in user needs due to the multilingual makeup of the home.

P7: Supporting Accessibility and Inclusivity in Field-based Education: Development of an Interactive Self-Guided Virtual Tour of a Geological Field School


Cynthia Liu, Research Assistant, EOAS, University of British Columbia
Laura Lukes, Assistant Professor, EOAS, University of British Columbia

Abstract:
During COVID-19, numerous instructors developed ways to incorporate digital technologies to create remote or virtual experiences to address students’ needs in geologic field courses (e.g., Burmeister et al., 2020). Despite a large volume of virtual field experiences created during 2020-2022 (see NAGT’s Teach the Earth Collection), there remains a limited number of studies focused on how such virtual field experiences can be effectively embedded to increase the accessibility and inclusivity of in-person field courses. Specifically of interest, is how virtual field experiences can be used to scaffold students for field-based learning by reducing anxiety and other learning barriers associated with the high levels of ‘novelty space’ (Orion and Hofstein, 1994). We developed an immersive self-guided virtual field trip (VFT) for students to interact with the environment at the Teck Geological Field Station (near Oliver, British Columbia, Canada) prior to arriving for their field course. The VFT was designed with the hypothesis that by frontloading students with key experience information about the station, students will feel more prepared and less anxious about embarking on a field-learning experience and associated living conditions. The VFT is thought to minimize cognitive overload and the negative effects on learning associated with novelty space, thus increasing students’ comfort and receptivity to field-based learning.

Here, we present an overview of the VFT design process, key design elements, lessons learned, and preliminary user experience data. The immersive VFT scenes combine 360° images, still images, Google Maps, audio clips, and informative text. After a comparative analysis of virtual tour platforms, we decided to create the VFT using Lapentor which allows students to study the site multimodally and embeds a mechanism for students to share feedback on their experience. This work can inform others seeking to design similar field course orientation materials.

P8: Unfolding Climate Stories: Kirigami Data Art in the Wild


Foroozan Daneshzand, PhD, Interactive Arts and Technology, Simon Fraser University.
Charles Perin, Assistant Professor, University of Victoria
Sheelagh Carpendale, Professor, Simon Fraser University

Abstract:
We used a fusion of kirigami (Japanese paper cutting) and data visualization to create three artistic and informative data physicalizations showcased at The Old School House Arts Centre on Vancouver Island from July to September 2022.

CO2 Giants: Total vs. Per Capita emission shows emission data for the seven largest CO2 producer regions: China, the U.S., the EU, India, Japan, Russia, and South Korea. In each pattern, the number of loops represents total CO2 emissions, and the number of joints corresponds to the population. The pattern expansion amount reveals per-capita data.

CO2 Giants: Emissions Through Time is a static data art piece depicting CO2 emission trends in China, the U.S., Russia, and Canada from 1975-2020. Volume changes from bottom to top reveal emission changes.

Heat Dome KiriPhys represents heat-dome-related deaths in B.C., Canada, during the summer of 2021. The size of circular patterns represents the city’s size, and the number of loops corresponds to the number of deaths. The number of joints represents the size of the population. Thus the expansion amount reveals deaths per-capita.

We observed visitor interactions for 30 hours, conducted several interviews, and engaged in conversations. Some of the findings are:

The interactive nature of the physicalizations fostered a sense of exploration and engagement, resulting in visitors spending more time with the installations.
Visitors were drawn to establish personal connections with the underlying data, actively seeking ways to relate, such as identifying their hometowns on the installations.
The innovative approach to data representation made visitors eager to explore ways to integrate this method into their lives and professions. For example, a teacher envisioned using it as a hands-on classroom activity to introduce students to datasets.
Children were especially enthralled by the installations, displaying enthusiasm and curiosity as they interacted with and explored the pieces.

P9: Data Exploration for Personally Collected Historical Data


Parnian Taghipour, Master, Computer science, Simon Fraser University
Michelle Levy, Professor, English, Simon Fraser University
Thomas Shermer, Professor, Computer science, Simon Fraser University
Sheelagh Carpendale, Professor, Computer science, Simon Fraser University

Abstract:
We are exploring how to visualize the Woman’s Print History Project (WPHP) data. This is a hand-assembled dataset about women in publication between 1700 and 1836. The researchers who have gathered this data are interested in understanding differences, inconsistencies and cultural variations in the data, which they have collected from different sources. Thus, there are inevitable gaps in the dataset, which reflect gaps in the historical knowledge. Also, since this data is modelling complicated human/object relationships over time and space, there are interesting data complexities. Besides, sometimes the type and nature of the variations and incompleteness can actually shed light on challenges women in print faced in the 1700 and 1800s. Thus, variations and incompleteness are an important part of the data to be visualized.

The main question is how can we visualize this data that has a lot of gaps. This visualization challenge is at the conflux of several active research directions in visualization. It is a collaboration with a domain expert. It is based on personally collected data and while personally collected data tends to be relatively small, this growing data set already has over 20,000 entries. While 20,000 entries, in terms of big data, are considered small, it is large enough to cause challenges in screen space usage and interactions. We are designing a prototype to help these experts explore their data while preserving its inconsistencies, thus allowing for a better understanding of historical events and societal trends. Our prototype includes three parallel timelines to show the number of events in a year and a parallel coordinate format that helps people identify relationships between the timelines. By providing navigation options to explore the complexities of this historical data, our project aims to enable researchers to gain new insights and perspectives that would otherwise be difficult to obtain.

P10: Examining the Effects of Instructional Precision on Exploratory Behaviours in Early Childhood


Aimee Lutrin, MA, University of British Columbia
Dr. Darko Odic, PI, University of British Columbia

Abstract:
Instructional messages and methods significantly affect children’s confidence and exploratory learning. Instructions can elicit specific behaviours from others and inform novel behaviours (e.g., how to ride a bicycle), both explicitly and implicitly. For example, the “double-edged sword” view of pedagogy posits that if children are given precise instructions (e.g., “press this button to make the toy light up”), the more confident they are in performing that function, but the less exploratory they tend to be of undemonstrated functions (Bonawitz et al, 2011). Despite psychological and educational understandings of instructional effects, little research bridges this literature across technological boundaries. We developed an iPad application that simulates a novel toy with many distinct functions, including obvious and hidden features (e.g., a button that emits a sound; a bar they can drag to make a light flash). As they interact with the application, children aged 4-8 recruited at a local science museum are given instructions of varying precision, depending on their condition. Exploratory learning is measured by the number of features discovered within a specific timeframe. We hypothesize that instructional precision affects exploration: participants who hear precise instructions should be less exploratory than those who hear vague instructions. Our preliminary findings of this research reveal broad implications for pedagogy: understanding the effects of instructional delivery will help early childhood educators and parents better design learning environments and create learning experiences more conducive to exploration. Broadly, this research project contributes to a necessary body of literature on the effects of instructional precision on experiential learning in early childhood.

P11: Data Comics for Understanding Climate Change


Zezhong Wang, Post-Doc, Computing Science School, Simon Fraser University
Stephan Gruber, Professor, Carleton University
Michelle Levy, Professor, Simon Fraser University
Sheelagh Carpendale, Professor, Simon Fraser University

Abstract:
While there is a gap between what the general public and policymakers understand about science and what is known by experts, this gap is particularly perilous regarding climate change. Climate change is increasingly recognized as a paramount threat to life on the planet. The most recent report from the Intergovernmental Panel on Climate Change highlights the extreme and worsening impacts of climate change, including rising sea levels, heatwaves, drought, flooding, regional food, and water shortages, storm damage, and more. Canada is particularly vulnerable to enormous disruptions: as Canada’s Changing Climate Report states, “Both past and future warming in Canada is, on average, about double the magnitude of global warming”. Scientists are generating massive amounts of data about climate change and developing significant understandings of the causal factors, wide-ranging projected impacts, and necessary mitigation and adaptation strategies. To know how to respond and make changes both policymakers and the general public need to be better supported to develop actionable comprehension. Currently, scientists inform each other via expert publications and conferences. We, as part of the public and policymakers, receive our information via the media and the web – and in our current catastrophic blending of information with misinformation, we are at risk of well-intentionally taking ineffective or even harmful actions and decisions. We need the best and most current scientific information in an easily accessible format that includes data transparency and is also both scientifically informed and verified. To close this gap, we have assembled a team that includes experts in data visualization, narrative construction, data comics, and climate change. We will work collaboratively to develop climate change data comics that combine compelling narratives with comprehensible data visuals that are informed and verified by the appropriate scientists.

P12: Making Friends through AI-Clone Chatbots


Merry Shirvani, MSc, Computer Science, University of British Columbia
Jackie Liu, MSc, Computer Science, University of British Columbia

Abstract:
Social anxiety is a pervasive challenge that hinders the formation of new friendships, not only in face-to-face interactions but also in computer-mediated relationships. This issue can significantly affect the quality of friendships and first impressions. With the recent advancements in artificial intelligence, there is now a unique opportunity to design for interactions that were previously impossible. In this study, we propose the use of AI clones as an intermediate step in the process of forming friendships to reduce anxiety during the initial stages of making friends. These clones would possess the ability to engage in conversations similar to the user after whom it is created. This work is an empirical investigation into the effects of such clones on reducing stress and the process, potential concerns, and challenges associated with this approach. To this aim, we recruited six participants and conducted a two-phased study, consisting of surveys and interviews. Utilizing thematic analysis, we identified various potential benefits of AI clones such as fascinating the initial stages, acting as a source of information, and reducing stress. However, this approach poses several drawbacks including concerns about impairing human connections, creation of additional barriers, misrepresentation, and privacy issues. Finally, we identified four design challenges and potential solutions for future work. Although opinions are divided on the usefulness of AI clones, this study highlights the potential of such technologies to offer a new and less stressful way of making friends.

P13: The LOVOT Robots as Companions for Older Adults


Hiro Ito, Master’s Student, IDEA Lab, University of British Columbia
Joey Wong, PhD Student, University of British Columbia
Dr. Lillian Hung, Assistant Professor, University of British Columbia

Abstract:
For this showcase, we will present our research study that aims to explore how a social robot (LOVOT) can be used to address loneliness and boredom as experienced by older adults in long term care homes. LOVOT is an award-winning AI robot from Japan that was designed by Groove X to foster connection and bring joy to users. This research study is part of a mixed-methods, three-country study conducted in Singapore, Hong Kong, and Canada. The focus of our poster presentation will be to (a) showcase the human-centred design features of LOVOT and its potential to bring psychosocial benefits to older adults and (b) introduce our research study and its implications for practice and research.

P14: Towards explainable visualizations


Maryam Rezaie, PhD student, ixlab, Simon Fraser University
Melanie Tory, Professor, ROUX institute, Northeastern University, United states
Sheelagh Carpendale, Professor, Simon Fraser university

Abstract:
Recently, there is a growing movement for Explainable AI (XAI), where one idea is to create AI tools whose decisions and prediction processes are visualized. To design effective explainable AI, we have to make sure that the visualizations are understandable and can effectively communicate the information. However, recent evidence suggests that the visualizations can be difficult to understand and there is a growing demand for more explainable visualizations. To that end, we first need to gain a better understanding of the support people require when attempting to make sense of visualizations.
Although explainable visualizations have not been formally introduced in the literature, several related concepts can help us in understanding the challenges, needs and existing solutions:
Visualization literacy: how to measure visualization literacy and solution for enhancing literacy
Visualization sensemaking process
Insight generation
Graph comprehension
Effectiveness of visualizations
Factors that affect previous activities
Existing solutions such as guidelines and tutorials
In the proposed poster, I review the aforementioned concepts and discuss how they can contribute to the design of a more explainable visualization. I also discuss the connections between these concepts.

P15: SMART-C: Enhancing C-Arm Fluoroscopy with Augmented Reality Overlays, Location Awareness, and Predictive Imaging


Parinaz Ranjbaran, MASc, Mechanical Engineering, University of British Columbia
Dr. Antony Hodgson, Professor, Department of Mechanical Engineering, University of British Columbia
Dr. Pierre Guy, Professor, Department of Orthopedics, University of British Columbia

Abstract:
Purpose
Our goal in this project is to markedly reduce radiation exposure and improve workflow efficiency in orthopaedic trauma surgeries by enhancing C-arm fluoroscopy with real-time X-ray video overlays.
Method
DeCAF uses a 3D depth camera mounted to the image intensifier tube of the C-arm to generate an augmented reality view using the recently acquired X-ray image overlaid on a live video view. The use of a pointer tool allowing depth measurement allows proper scaling of the X-ray image within video feed. The system allows the surgeon to easily and intuitively understand the relationship between the patient’s surface anatomy, surgical tools in the operating field, and anatomical and surgical structures within the patient.

Results
The X-ray overlays are shown in a couple of examples in Figure 1. We found that the overlay accuracy is 1.58 ±0.37mm at target-II distance of 270 – 430 mm, the typical anatomical distance to image-intensifier in many orthoapedic procedures, meaning that there’s no significant dependence of accuracy on depth.

Conclusions
In this work, the main functionalities for the DeCAF system to create real-time X-ray overlays have been developed. The overlay accuracy is found to be acceptable for further clinical studies in which the impact of the system in terms of radiation exposure and time is evaluated in simulated lab and OR clinical studies. Our insight is that improved perception of the relative positions of surgical tools and the patient’s anatomy would enable the surgeon to avoid taking multiple X-ray images to verify the relative position of their tools and the anatomy.

P16: Investigating Techniques to Spatially Locate Frames of a 2D Ultrasound Image Sequence for the Application of Developmental Dysplasia of the Hip


Ammarah Kaderdina, MASc, University of British Columbia
Maria Jose Bonta Suarez, MASc, University of British Columbia
Rafeef Garbi, Professor, University of British Columbia
Emily Schaeffer, Research Director, British Columbia Children’s Hospital
Kishor Mulpuri, Pediatric Orthopaedic Surgeon, British Columbia Children’s Hospital
Antony Hodgson, Professor and Associate Head, University of British Columbia

Abstract:
Developmental dysplasia of the hip (DDH) is the most common orthopedic disorder found in infants. When left untreated, the surrounding anatomy develops abnormally which increases the risk of developing hip osteoarthritis later in life. Physicians currently use 2D ultrasound (US) to screen for DDH. Using 3D US has shown to markedly reduce inter-rater variability, however 3D US scanners are not widely available in pediatric clinics. Instead, we propose reconstructing a 3D US volume based on a sequence of spatially located US image frames. We investigate several techniques for spatially locating frames: optical tracking, deep learning, and using an inertial sensor. The aim of this project is to determine which technique is optimal for the application of DDH when comparing cost, accuracy, and obtrusiveness. The long-term goal is to implement the proposed approach into the clinic, where a physician would input a sweep of 2D ultrasound images collected over the infant hip to an online tool, to obtain an automatic 3D ultrasound reconstruction and DDH metric extraction.

P17: Design Solutions to Reduce Miscommunication in Videoconferencing Meetings


Ying Chen, MLIS, iSchool, University of British Columbia
Dr. Luanne Sinnamon, Associate Professor, iSchool, University of British Columbia

Abstract:
The COVID-19 pandemic has propelled the adoption of VC meetings and remote work that is still prevalent today. Its impacts include Zoom fatigue, Zoom anxiety and limited social interactions in virtual teams, all of which affect employees’ workplace wellbeing. Videoconferencing (VC) meetings are prone to miscommunication due to participants’ lack of co-presence, limited non-verbal communication cues and common technical difficulties. Miscommunication – misunderstandings between participants when the speaker fails to produce the intended effect or when the hearer cannot recognize what the speaker intends to communicate, or both – are likely to occur in this setting, thus impacting the quality of communication in virtual teams. Past research on miscommunication in VC meetings is limited; common technical issues and human factors have been found to be the causes of miscommunication in such meetings, and little work has investigated the details of miscommunication experiences in this new era of post-pandemic remote work. This study therefore aims to fill this research gap by using the Research through Design methodology with the design workbook method to investigate users’ experiences of miscommunication in VC meetings, provide speculative sketches of possible design solutions to reduce miscommunication, and understand users’ attitudes towards and expectations of such solutions. This poster reports on the first phase of the project in which we conducted interviews with 14 office workers to ask about their experiences participating in VC meetings. This data is being analyzed using the Reflexive Thematic Analysis method to elicit common themes and design opportunities. In future steps, the researchers will create speculative sketches of design solutions to reduce miscommunication, which will be used in the second interview to elicit participants’ reactions, ratings and rankings of these solutions.

P18: The Effect of Research Video Abstract Presentation on Viewer Comprehension and Engagement


Alice Li, PhD, iSchool, University of British Columbia
Heather O’Brien, Associate Professor, iSchool, University of British Columbia
Luanne Sinnamon, Associate Professor, iSchool, University of British Columbia

Abstract:
Video abstracts (VA) is a short audiovisual of research output. The importance of making research accessible to non-academic audiences is becoming widely recognized, and VA is a promising format for achieving this goal. How can we design VAs to increase research awareness, understanding, and uptake? Previous studies have focused on user interactions with VAs in the sciences. It is unclear what makes an effective social science VA, and how viewers interact with them measured by established comprehension frameworks and valid engagement questionnaires. This poster summarizes a crowdsourced experiment that examined the effect of two VA presentation styles (slideshow, animation) on the comprehension of and engagement with social science research.

Demos

D1: DELVE into the Past: A Visualization-Based Exhibit for Teaching Museum Visitors about Deep Time


Mara Solen, PhD student, CS, University of British Columbia
Nigar Sultana, MSc student, EOAS, University of British Columbia
Laura Lukes, Assistant Professor, EOAS, University of British Columbia
Tamara Munzner, Professor, CS, University of British Columbia

Abstract:
Visitors to museums with exhibits about the Earth’s past often struggle to conceptualize deep time, the extremely long time periods in Earth’s past, which are in use in these displays. To support visitors in understanding these exhibits, we present the Deep-time Literacy Visualization Exhibit (DELVE), a work-in-progress, interactive, guided visualization of geologic time built for use in museums. DELVE visualizes carefully curated datasets of events from the history of the Earth on multiple scales at once with the intent of teaching visitors to understand the scale of deep time. The exhibit employs a newly-developed visualization technique that uses datasets of events which are evenly distributed across logarithmically partitioned, disjoint, contiguous, and monotonically increasing periods of time. DELVE is currently deployed in the Beaty Biodiversity Museum at UBC and we are conducting an observational study to learn about exhibit usage and iterate on our design. Once design iteration is complete or near complete, we will conduct more systematic and targeted studies investigating visitor experience and learning outcomes from interacting with DELVE.

D2: ESSbots: Designing emotionally supportive swarm robots for remote social connection


Elizabeth Reid, Masters, Computer Science, University of British Columbia

Abstract:
This work explores how teenage friend groups can utilize swarm robots for affective and embodied remote communication. Through a series of participatory design workshops, we developed and iterated upon a set of swarm behaviours and interactions that promote emotional connection and communication. We discuss our current results in our ongoing workshop series, as well as important design considerations for an emotionally supportive swarm robot platform that can support our goal of embodied and affective interaction and also allow for easy access to new behaviour creation and control by teens. In our accompanying demo, we demonstrate two forms of robot behaviour creation and control as shown to participants in our second workshop: minimally customizable pre-set behaviour buttons, and highly customizable visual scripting, both of which participants viewed as desirable in different situations of use.

D3: The Experiences of Using Telepresence Robots by Residents with Dementia at Long Term Care Homes and their families


Lillian Hung, RN, PhD; Principal Investigator of the Telepresence Robot; Assistant Professor, School of Nursing, University of British Columbia; Canada Research Chair in Senior Care
Lily Ren Haopu, Telepresence Robot Project Manager, PhD Student, Innovation in DEmentia and Aging (IDEA) Lab, University of British Columbia
Jason Fu, Telepresence Robot Project Technology Support, BSc Candidate, Innovation in DEmentia and Aging (IDEA) Lab, University of British Columbia
Joey Wong, Telepresence Robot Project Manager, PhD Student, Innovation in DEmentia and Aging (IDEA) Lab, University of British Columbia
Grace Hu, BSc Candidate, Innovation in DEmentia and Aging (IDEA) Lab, University of British Columbia
Nazia Ahmed, MSc, University of British Columbia
Ali Hussein, BSc Candidate, Innovation in DEmentia and Aging (IDEA) Lab
Jim Mann, Patient Partner, Honorary Doctor of Laws degree (LL.D.) Innovation in DEmentia and Aging (IDEA) Lab

Abstract:
The Challenge
Residents in long-term care (LTC) are at risk of social isolation and reduced social connection due to shrinking social networks, communication, and physical challenges, and limited opportunities for social engagement and novel relationship-building. The pandemic has exacerbated the reduced social connection of residents, which has negative effects on their social, emotional, and mental well-being.

Our Telepresence Robot Project
We have implemented telepresence robots in four LTC sides in BC to explore a more accessible option for virtual video calls for residents and families. We work with industrial partners, staff, residents, families to make adaptations.

The Human Centeredness Design
Telepresence robots have the following three advantages to address the diverse needs of residents with dementia.
(1) No effort is required on the resident’s side. The robot could be fully controlled by the families for their virtual visitations.
(2) The mobility of the robot makes engagement of residents with dementia easier. Through the screen, the families could see their loves ones and the living environment.
(3) Family members can initiate calls without requiring staff assistance.

Experiences Reported
We identified five themes (three positive experiences and two challenges) characterizing how families and residents felt about the use of telepresence robots in these sites.
Theme 1: “We are Together in Different Places”: The Robot Helps Residents Stay Connected to Their Families
Theme 2: “Freedom Makes a Big Difference”: The Robot Supports a Sense of Autonomy
Theme 3: “We Can Have Peace of Mind” – the Robot Decreases Care Partner Burden
Challenge 1: “The Robot was Stuck” – Environmental Challenges in LTC
Challenge 2: “I Don’t Know Whether My Dad is Available or Not” – Scheduling Issues

Conclusion
Telerobot can help support the connection between residents and families, and reduce caregiver burden. The study offers insight into future research in the adoption

D4: Designing an Informal Remote Computing Assistance Platform for Older Adults


Teerapaun Tanprasert, PhD, Computer Science, University of British Columbia
Joanna McGrenere, Faculty, Computer Science, University of British Columbia

Abstract:
The use of technology among older adults has been on the rise in recent years, and there has been a growing interest in understanding how they learn and obtain support for their computing needs. One common method is remote video-mediated assistance provided by younger, more tech-savvy family members. However, currently available communication tools are not designed with the unique needs of older adults in mind, and lack the features required for effective remote learning support. In this project, we designed and evaluated an augmented video-conferencing platform that enables older adults to receive remote computer assistance from their friends or family members in a comfortable and effective way. We then conducted a structured observation study with 14 older adults, comparing our design to basic Zoom. We found that participants showed an overall positive reception, with a strong preference for one of our two design variants, and identified several important insights into their behaviors when seeking software help. We are currently developing a high-fidelity prototype and planning further studies to address outstanding questions.

D5: Feel the Shift: A Multimodal Learning Experience for Manual Transmission


Bereket Guta, MSc Student, Computer Science, University of British Columbia
Tommy Nguyen, MSc Student, Computer Science, University of British Columbia
Jano Fu, MSc Student, Computer Engineering, McGill University

Abstract:
Drivers attempting to learn manual transmission (MT) face simultaneous straining stimuli leading to safety concerns and diminished confidence behind the wheel. Integrating active haptics through deliberate force feedback into a MT simulator has the potential to simulate the experience and promote good driving habits. We provide an accessible at-home MT simulator grounded in haptic experience design to allow drivers to explore MT and develop the necessary shifting rhythm—reinforced through active haptics. We package our solution in a gaming context, leveraging visual and auditory modalities, in addition to haptics, to enhance the learning experience

D6: The Digital Human Library Prototype: Designing for Inclusivity and Accessibility in Mental Health Support for University Students


Sang-Wha SIen, PhD, Computer Science, University of British Columbia

D7: Neonatal Heart Monitoring using Video Cameras


Ethan Grooby, PhD, Monash University and the University of British Columbia
Chiranjibi Sitaula, Research Fellow, University of Melbourne
Pascal M Lavoie, Clinician, BC Children’s Hospital
Liisa Holsti, Professor, University of British Columbia
Atul Malhotra, Clinician, Monash Children’s Hospital
Faezeh Marzbanrad, Lecturer, Monash University
Guy Dumont, Professor, University of British Columbia

Abstract:
Background: The first month of life (neonatal period) is a vulnerable stage of life. Monitoring and assessment for signs of serious health problems are essential for timely and adequate care.

Aim: Investigate the feasibility of using video cameras as a non-contact means for heart monitoring in the clinical setting.

Methods: A four-step process was implemented: (1) region of interest detection, (2) region of interest tracking, (3) photoplethysmogram extraction, and (4) identification of heart peaks for heart rate and heart rate variability calculations.

These steps were tested on 3 publicly available datasets of neonatal images/videos, which had a variety of head poses, facial expressions, and ethnicities.

Results/Conclusion:
(1) Adapted YOLO-based face and landmark detector superior to existing works for the region of interest and landmark detection
(2) Modified feature tracker suitable for the region of interest and landmark tracking for minor movements
(4) Heart rate estimation results are promising but further work is required
(4) Heart rate variability estimations appear to not be feasible with the current methods and camera setup.

D8: Learning Programming Concepts with Tangibles: A Demo


David Wong-Aitken
Parsa Rajabi, MsC Student, Simon Fraser University
Sheelagh Carpendale, Associate Professor, Simon Fraser University
Parmit Chilana, Associate Professor, Simon Fraser University

Abstract:
The need for programming skills has led to a demand for teaching approaches that can appeal to a broader audience. TangiBooks is a paper and electronics platform that uses tangible objects and sensory interactions to make introductory programming concepts more engaging. It offers four lessons on key concepts like algorithms, variables, conditionals, and loops. TangiBooks is considered playful, appealing to learners’ senses, and has potential pedagogical benefits for promoting reflection among learners. The platform may give guidance to researchers in furthering the design of paper-based tangibles for learning and empowering instructors to innovate and personalize lessons.

D9: Improving In-class Multitasking in Online Synchronous Classes


Sahar Mavali, MASc., Department of Electrical and Computer Engineering, University of British Columbia
Dongwook Yoon, Assistant Professor, Department of Computer Science, University of British Columbia
Luanne Sinnamon, Associate Professor, iSchool, University of British Columbia
Sid Fels, Professor, Department of Electrical and Computer Engineering, University of British Columbia

Abstract:
Multitasking in online classrooms is extremely common among university students. Research suggests that in-class multitasking impairs students’ academic performance. However, despite being aware of its potential consequences, university students often prioritize other tasks over the class content to keep up with their many responsibilities. In this paper, we propose to address these consequences by designing a user interface that supports in-class multitasking rather than preventing it. We ran a formative user study to discover the main challenges students face with in-class multitasking. We learned that multitasking reduces students’ cognitive capacity and inhibits their metacognitive regulation. We prototyped a user interface that supports multitasking in online synchronous classes by creating a bichronous online learning environment that allows immediate access to missed content by combing synchronous and asynchronous learning. Guided accelerated viewing of the missed content helps students improve their learning without exceeding the time frame of the class. Our interface also assists students by providing time and content-based alerts to ensure proper attention management and metacognitive regulation.

D10: A Tool for Capturing Customized Multi-Pass Emotion Self-Reports while Touching and Telling


Rúbia Reis Guerra, PhD student, Computer Science , UBC
Laura Cang, PhD student, Computer Science , UBC
Nao Rojas
Daniel Chen
Bereket Guta, MSc Student, Computer Science, UBC
Karon E. MacLean, Faculty, Computer Science, UBC

Abstract:
Human emotions are complex, dynamic, and individualistic experiences that evolve over time, making it challenging for devices to accurately estimate personal emotions in real-time. Traditional emotion models that assume generalized emotions exist as discrete states fail to capture the valuable information inherent in the dynamic nature of emotions. We present an interface that supports multi-resolution emotion self-reporting procedures, demonstrating the construction of emotion labels along a custom emotion scale at the rate of emotion change. This procedure differentiates not only what the emotions are, but also how they are transitioning, enabling the identification of emotions such as “hopeful but getting stressed” vs “hopeful and starting to relax”.

554K Project Videos

V1: Vital signs Integration System to Improve Outcomes in Neonates (VISION)


Rosalyn Carr, MASc, Biomedical Engineering, University of British Columbia
Emily Chan, MLIS, iSchool, University of British Columbia
Jackie Liu, MSc, Computer Science, University of British Columbia
Ryan Smith, PhD, Computer Science, University of British Columbia
Titilola Yakubu, MSc, Experimental Medicine

Abstract:
Fragile infants in the neonatal intensive care unit at BC Children’s and Women’s Hospital require constant vital signs monitoring to inform clinicians’ timely life-saving decisions. Currently tracking and presenting long term vital signs to facilitate necessary patient wellness classification and trends analysis is manual and time intensive for doctors, nurses and respiratory therapists. Our opportunity to capture patients’ stability over time with continuous vital signs data to support neonatologists’ decision-making in care planning was clarified through our research findings. Guided by user needs and design requirements abstracted from our research and a design thinking methodology, we developed an automated-summarized-physiological data tool that supports the assessment of infant stability over longer periods of time.

Our early concept for a NICU patient planning and assessment interface for neonatologists highlights design considerations for NICU technology and contributes to future ethnographic research in the area. Because this type of tool would also be used by clinicians from multiple disciplines a variety of clinicians, more research and user testing with all clinicians is needed to abstract comprehensive user needs, challenges, and tasks required to implement a prototype into the environment for evaluation. With this holistic approach and strong collaboration with users, we believe the concept could be improved to support clinicians’ decision-making in care planning.

V2: Envisioning Healthcare Supply Chain Dashboard


Ishita Haque, MSc, Computer Science, University of British Columbia
Michelle Chen, MSc, iSchool, University of British Columbia
Tommy Nguyen, MSc, Computer Science, University of British Columbia
Vedant Bahel, MSc, Computer Science, University of British Columbia

Abstract:
Recent pandemic have highlighted several inefficiencies in healthcare supply chain management, such as inefficient and manual communication, a lack of a system to flag and prioritize disruptions, and a lengthy process to search for replacement products. We aim to develop a dashboard to manage supply disruptions efficiently and mitigate the impact of disruptions on patient care.
The project had four phases: Empathize, Define, Ideate, and Prototype. In the Empathize phase, the team used user stories and proto-personas to understand the problem domain better. In the Define phase, the team narrowed down the problems and finalized the requirements for a dashboard. In the Ideate phase, the team utilized the MoSCoW prioritization analysis matrix to categorize the ideated features into must-have, should-have, could-have, and won’t-have categories. In the Prototype phase, the team created a low-fidelity prototype featuring a login screen, dashboard home, supply disruptions table view, and detailed view. The team also conducted a cognitive walkthrough with the project partner and received feedback for improvement.
The dashboard has role-based views, automated notification management, and progress tracking to streamline communication and provide real-time information. The home page provides insight into meetings, tasks, and notifications, while the supply disruptions table view allows stakeholders to search and filter supply disruptions. The detailed view provides meta-information to track progress and allows suggestions and approvals for alternative supplies. The changelog tab is a history of changes made throughout the resolution process. In the cognitive walkthrough, the participant evaluated their experience with the current dashboard and our prototype. The prototype experience was significantly better, decreasing the time-on-task, the difficulty in accessing information and tracking communication, and increasing the clarity of the process.

V3: Self-guided Immersive VR Cognitive Training


Merry Shirvani, Master, Computer Science, University of British Columbia
Zhe Liu, PhD-track, Computer Science, University of British Columbia
Pegah Derakhshanfar, PhD, Rehabilitation Science, University of British Columbia
Himani Prajapati, PhD, Rehabilitation Science, University of British Columbia

Abstract:
Virtual reality (VR) technology has emerged as a promising alternative to traditional pen-and-paper approaches for cognitive rehabilitation in mental health disorders. In this context, the Cognitive Health Technologies (CHT) team at the National Research Council Canada (NRC) has developed bWell, a state-of-the-art VR application specifically designed to improve cognitive capabilities in patients with depression. bWell provides a suite of eight immersive cognitive exercises that target different cognitive domains. Following the initial validation of the prototype, the CHT team transformed it into a self-administrative mode, with the intention of exploring the potential for patients to use it at home independently. In this study, we evaluate the usability of bWell through a three-phase process, including Cognitive Walkthrough, Expert Heuristic Evaluation, and Usability Testing sessions. The results shed light on users’ demands and habits regarding VR-based self-administrative cognitive applications and inform the design of future interventions.

V4: Form and Function: Redesigning the University Canada West (UCW) Library Homepage


Alice Li, PhD, iSchool, University of British Columbia
Onyekachukwu Odenigbo, PhD, Civil Engineering, University of British Columbia
Kira Razzo, Master’s, iSchool, University of British Columbia

Abstract:
Over the course of our research into redesigning the University Canada West (UCW)’s library homepage, our team has been able to identify key student problems with the previous homepage through surveys, interviews, and prototype testing, and collect feedback on what should be included in any future redesign. Students were facing three major struggles: 1) difficulty navigating the site due to an overwhelming amount of links on the homepage, 2) confusion about how to cite in APA format (which stemmed from not being able to find relevant resources on the site to help with formatting), and 3) an inability to effectively conduct a database search due to a lack of knowledge on library database searching.

To address these problems, we conceptualized and designed a medium-fidelity prototype, and tested it with students and library staff for feedback. We have consolidated this feedback into a list of suggestions which we feel should be considered for UCW’s library homepage redesign. Overall, we suggest combining the med-fidelity prototype layout with the new UCW website layout changes (i.e., the left-hand menu bar) implemented in April 2023. Students mentioned they liked certain aspects of both. Other suggestions we have include:

1. Incorporating an interactive search bar for upcoming workshops and how to sign up, which would be helpful for students to know what’s coming up and what they can attend;

2. A left side menu that includes main heading and subheadings, and top headings which categorize the different resources the library provides, as students found both menus useful, and;

3. A customizable/interactive dashboard with a frequently searched widget.

4. The APA resource page needs to be redesigned, as students had trouble finding the citation format videos embedded within the APA resource page. Students need to click on the quick guide to find the answers.

V5: HarrietGo: Empowering Citizen Science


Matt I.B. Oddo, PhD Student, Computer Science, University of British Columbia
Ying Chen, MLIS Student, iSchool, University of British Columbia
Jason Hall, MSc Student, Computer Science, University of British Columbia
Bereket Guta, MSc Student, Computer Science, University of British Columbia

Abstract:
Harriet the Herring is a mascot of our sponsor, Átl’ḵa7tsem/Howe Sound Marine Stewardship Initiative (MSI). Herring is a key species of the region’s ecology, yet much of their life cycle is still a mystery. To fill these knowledge gaps, MSI recruits volunteers to scout the Squamish shoreline to develop a comprehensive dataset on herring life history through their rare spawn events, which are significant as they mark the end of winter, akin to spring blooms under the waves. Aligned with MSI’s mission, we present HarrietGo, a citizen science data collection app. At the core of the app is a novel data structure that allows a concurrent stream of quantitative and qualitative data by citizen science users. This data structure underpins our visual interface, designed for ease of place-based data collection; and our novel server hub, designed for data archiving and long-term analysis. As a citizen science platform, HarrietGo fosters input from the community, further enriching what users share with features such as supplemental data, leaderboard volunteer engagement, and the export of reports. We successfully validated our data structure with a GPS-enabled smartphone Python prototype, and our visual interface through a usability study (N=4). Our findings show positive results in terms of ease of use, system consistency, and learnability of our medium-fidelity prototype.