Programming skills have been considered more important than ever for people to thrive in the age of the digital economy. Meanwhile, computers have become ubiquitous in our life and work, and the way they are programmed is in need of fundamental improvements. In this talk, I introduce research on creating toolkits and integrated development environments that help people to create, edit, and make use of programs, and discuss the future of programming.
Jun Kato is a Human-Computer Interaction researcher at National Institute of Advanced Industrial Science and Technology (AIST), Japan. He has focused on improving Programming Experience (PX) by creating toolkits and integrated development environments. He has worked for Microsoft and Adobe Research and received a Ph.D. from The University of Tokyo under the supervision of Prof. Takeo Igarashi in 2014.
Mobile and ubiquitous computing research has led to new techniques for cheaply, accurately, and continuously collecting data on human behavior that include detailed measurements of physical activities, social interactions and conversations, sleep quality and duration and more. Continuous and unobtrusive sensing of behaviors has tremendous potential to support the lifelong management of mental health by: (1) acting as an early warning system to detect changes in mental well-being, (2) delivering context-aware, personalized micro-interventions to patients when and where they need them, and (3) by significantly accelerating patient understanding of their illness. In this presentation, I will give an overview of our work on turning sensor-enabled mobile devices into well-being monitors and instruments for administering real-time/real-place interventions.
Tanzeem Choudhury is an associate professor in Computing and Information Sciences at Cornell University and a co-founder of HealthRhythms. At Cornell, she directs the People-Aware Computing group, which works on inventing the future of technology-assisted wellbeing. Tanzeem received her PhD from the Media Laboratory at MIT. Tanzeem was awarded the MIT Technology Review TR35 award, NSF CAREER award and a TED Fellowship. Follow the group's work on twitter @pac_cornell
Despite substantial effort made by the usable security community at facilitating the use of recommended security systems and behaviors, much security advice is ignored and many security systems are underutilized. I argue that this disconnect can partially be explained by the fact that security behaviors have myriad unaccounted for social consequences. For example, by using two- factor authentication, one might be perceived as “paranoid”. By encrypting an e-mail correspondence, one might be perceived as having something to hide. Yet, to date, little theoretical work in usable security has applied theory from social psychology to understand how these social consequences affect people’s security behaviors. Likewise, little systems work in usable security has taken social factors into consideration.
To bridge these gaps in literature and practice, I begin to build a theory of social cybersecurity and apply these theoretical insights to create systems that encourage better cybersecurity behaviors. First, through a series of interviews, surveys and a large-scale analysis of how security tools diffuse through the social networks of 1.5 million Facebook users, I constructed empirical models of how social influences affect the adoption of recommended security behaviors and systems. In so doing, I provide some of the first direct empirical evidence that security behaviors are strongly driven by social influences, and that the design of a security system strongly influences its potential for social spread. Specifically, security systems that are more observable, inclusive, and stewarded are positively affected by social influence, while those that are not are negatively affected by social influence.
Based on these empirical results, I put forth two prescriptions: (i) creating socially grounded interface “nudges” that encourage better cybersecurity behaviors, and (ii) designing new, more socially intelligent end-user facing security systems. As an example of a social “nudge”, I designed a notification that informs Facebook users that their friends use optional security systems to protect their own accounts. In an experimental evaluation with 50,000 Facebook users, I found that this social notification was significantly more effective than a non-social control notification at attracting clicks to improve account security and in motivating the adoption of optional security tools. As an example of a socially intelligent cybersecurity system, I designed Thumprint: an inclusive authentication system that authenticates and identifies individual group members of a small, local group through a single, shared secret knock. Through my evaluations, I found that Thumprint is reasonably resilient to casual but motivated adversaries and that it can reliably differentiate multiple group members who share the same secret knock. Taken together, these systems point towards a future of socially intelligent cybersecurity that encourages better security behaviors.
Concretely, this thesis provides the following contributions: (i) an initial theory of social cybersecurity, developed from both observational and experimental work, that explains how social influences affect security behaviors; (ii) a set of design recommendations for creating socially intelligent security systems that encourage better cybersecurity behaviors; (iii) the design, implementation and comprehensive evaluation of two such systems that leverage these design recommendations; and (iv) a reflection on how the insights uncovered in this work can be utilized alongside broader design considerations in HCI, security and design to create an infrastructure of useful, usable and socially intelligent cybersecurity systems.
Jason Hong (Chair)
Laura Dabbish (Co-chair)
J.D. Tygar (University of California, Berkeley)
11:30 am — Introductions to the BHCI program
11:45 am — Refugees: Cultural Orientation for Refugees & Immigrants
11:57 am — ACS: Canceer Survivors Network: BUilding a New Community Interface
12:09 pm — ATV: Interface for an Off-road Autonomous Vehicle
12:21 pm — Refining RoboTutor: machine-aided design iteration of an intelligent tutor
12:33 pm — Vocab: Promoting Children's Vocabulary Growth
12:45 pm — Animal Cop: Reporting Animal Abuse
1:00 pm — HCII Lunch - Wean Patio
1:00 pm — Vocab: Promiting Children's Vocabulary Growth
1:30 pm — ACS: Cancer Survicors Network: Building a New Community Interface
2:00 pm — ATV: Interface for an Off-road Autonomous Vehicle
2:30 pm — Refugees: Cultural Orientation for Refugees & Immigrants
3:00 pm — Refining RoboTutor: Machine-aided Design Iteration of an Intelligent Tutor
3:30 pm — Animal Cop: Reporting Animal Abuse
SCS community welcomed.
Across a wide variety of digital devices, users create, consume, and disseminate large quantities of information. While data sometimes look like a spreadsheet or network diagram, more often for everyday users their data look more like an Amazon search page, the line-up for a fantasy football team, or a set of Yelp reviews. However, interpreting these kinds of data remains a difficult task even for experts since they often feature soft or unknown constraints (e.g. "I want some Thai food, but I also want a good bargain") across highly multidimensional data (i.e. rating, reviews, popularity, proximity). Existing technology is largely optimized for users with hard criteria and satisfiable constraints, and consumer systems often use representations better suited for browsing than sensemaking.
In this thesis I explore ways to support soft constraint decision-making and exploratory data analysis by giving users tools that show fine-grained features of the data while at the same time displaying useful contextual information. I describe approaches for representing collaborative content history and working behavior that reveal both individual and group/dataset level features. Using these approaches, I investigate general visualizations that utilize physics to help even inexperienced users find small and large trends in multivariate data. I describe the transition of physics-based visualization from the research space into the commercial space through a startup company, and the insights that emerged both from interviews with experts in a wide variety of industries during commercialization and from a comparative lab study. Taking one core use case from commercialization, consumer search, I develop a prototype, Fractal, which helps users explore and apply constraints to Yelp data at a variety of scales by curating and representing individual-, group-, and dataset-level features. Through a user study and theoretical model I consider how the prototype can best aide users throughout the sensemaking process.
My dissertation further investigates physics-based approaches for represent multivariate data, and explores how the user's exploration process itself can help dynamically to refine the search process and visual representation. I demonstrate that selectively representing points using clusters can extend physics-based visualizations across a variety of data scales, and help users make sense of data at scales that might otherwise overload them. My model provides a framework for stitching together a model of user interest and data features, unsupervised clustering, and visual representations for exploratory data visualization. The implications from commercialization are more broad, giving insight into why research in the visualization space is/isn't adopted by industry, a variety of real-world use cases for multivariate exploratory data analysis, an index of common data visualization needs in industry, and even some helpful tips for research-inspired startups.
Aniket Kittur (Chair)
John Stasko (Georgia Institute of Technology)
As computing devices continue to evolve from personal computers to mobile and wearable technologies, new learning opportunities are opening up. Specifically, we have been interested in supporting science learning, and considering how a range of these technologies can be designed in a supportive manner to support such learning across multiple contexts. Our Zydeco project has explored the coordinated, integrated use of mobile devices (e.g., smartphones and tablets), web applications, and the cloud to support middle school students with science practices across formal classroom and informal out-of-class (e.g., museums and parks) contexts. Zydeco helps students plan their inquiry in classrooms, collect different data artifacts (e.g., photos, videos, audios and texts) outside the classroom, and analyze those artifacts to build a scientific explanation, thus framing an activity structure that integrates different contexts into a larger educational setting.
As we have developed Zydeco, we have also examined how particular design methods, such as Luckin's Ecology of Resources approach, can help identify factors, conflicts, and challenges that arise when designing supportive tools for cross-context use. Working with middle school teachers, students, and museum educators, we have identified different issues when trying to create a more cohesive fit between the formal and informal contexts where students will engage in their scientific activity: (1) resource fit, or the challenge teachers face in understanding what resources are available in out-of-class contexts and how those resources connect to their curricula and current science standards, (2) supportive fit, or the scaffolding features that need to be developed and embedded in the mobile tools, museum exhibits, and other resources to support students with the reflective and analytic activity needed for sensemaking with respect to their science questions and goals, and (3) cultural fit, or the potential mismatch between the goals of structured and free-choice contexts, the way these different goals impact exploratory activity, and the anxieties about technology that arise in many informal contexts. In this presentation, I will give an overview of the Zydeco project and software, and discuss the Ecology of Resources model and consider how this and similar models are needed to help identify various elements and issues for cross-context design.
Chris Quintana engages in research that is at the intersection of education and learning sciences, human-computer interaction, and computer science. He has focused much of work on software-based scaffolding for middle school science students, including the development of scaffolded software tools, scaffolding frameworks for software, and learner-centered design processes. His recent work includes heading the Zydeco Project, which was funded by the National Science Foundation (NSF) to explore how mobile devices and web-based technologies can be integrated to connect science classrooms and museums to expand science learning opportunities. Using Zydeco, Quintana is exploring not just how software tools can support students with various science practices in different contexts, but also the possibilities and challenges of developing learning activities that integrate formal and informal learning environments. Other recent work includes exploring the use of wearable technologies (e.g., smartwatches) by K-12 teachers in their classrooms to help monitor student activity and the classroom envionrment.
His previous work involved working as a principal investigator in the Center for Highly Interactive Classrooms, Curricula, and Computing in Education (hi-ce), where he worked on several learning technology projects. Quintana previously led NSF-funded projects focused on developing and assessing software that supports students with different inquiry-based practices, such as the creation of software- based “digital ideakeepers” to support students in analyzing and synthesizing information found in digital libraries to answer science questions. He was on the research team for a project focusing on how media-rich digital texts that follow a “universal design for learning” approach may impact science learning. Other previous projects that Quintana has worked on include the ASSESS project to develop a “scaffolding design framework” to guide developers and researchers of learning technologies, and the Symphony2 project to develop a software framework that could be used to build scaffolded work environments.
Aside from developing and researching different types of learner-centered software, Quintana is also interested in design processes and the notion of “design thinking” for education. His design activity informs his courses on the design and assessment of learning technologies, and other work exploring the development of new technology-enhanced learning spaces within the School of Education. Quintana received his BS from the University of Texas at El Paso in Biological Sciences, and his MS and PhD from the University of Michigan in Computer Science and Engineering.
Faculty Host: Amy Ogan