Brown Bag Lunch Schedule
The HCIL has an open, semi-organized weekly "brown bag lunch (BBL)" every Thursdays from 12:30-1:30pm in HCIL (2105 Hornbake, South Wing). The topics range from someone's work, current interests in the HCIL, software demos/reviews, study design, proposed research topics, introductions to new people, etc. The BBL is the one hour a week where we all come together--thus, it’s a unique time for HCIL members with unique opportunities to help build collaborations, increase awareness of each other’s activities, and generally just have a bit of fun together. There is no RSVP; simply show up!
If you would like to give or suggest a talk, presentation, workshop, etc., send an email to BBL student co-coordinators Sriram Karthik Badam (firstname.lastname@example.org) or Pavithra Ramasamy (email@example.com). In the email, briefly describe the topic and preferred dates.
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Fall 2017 Schedule
Kickoff to a new Semester!
Come network, make introductions, and share what each of us is working on
Please come to our first BBL of the Fall 2017 semester to introduce yourself and share what you're working on in the coming semester. The first BBL will be for us to network with each other and kickoff a great new semester.
David Weintrop, University of Maryland, College Park
To block or not to block: Understanding the effects of programming language representation in high school computer science classrooms.
Abstract: In the last few years, Chicago, New York City, and San Francisco have all announced major initiatives to bring computer science classes and computational thinking into every high school in their cities - with countless other smaller school districts following suit. Having made these commitments, attention now shifts towards how best to teach computer science to diverse populations of high school students who grew up in the age of smart phones, iPads, and Facebook. An increasingly popular strategy being employed is the use of graphical, block-based programming environments like Scratch, Blockly, and Alice. While these environments have been found to be effective at broadening participation with younger learners, open questions remain about their suitability in high school contexts. In this talk, I will present findings from a two-year classroom study looking at how the design of introductory programming environments affects learners' emerging understandings of computer science concepts and their perceptions of the field of computer science. I will also discuss the affordances of block-based programming environments relative to more conventional text-based alternatives.
From Independence to Interdependence: A Social Narrative of Assistive Technology
Abstract: In the Assistive Technology and greater disabilities community, “independence” has been a core goal and frame for making progress toward equality. This dominant narrative is often interpreted to mean that disabled people can and should live independently without help from others, and that assistive devices exist to displace reliance on helpers. For example, a wearable device that gives a blind person turn-by-turn directions through an airport displaces a sighted human guide. However, my work with people with disabilities in the home, in the workplace, and in public spaces has demonstrated that collaboration is a significant tool and goal of people with disabilities in their everyday lives. Further, social setting and human-human interactions significantly impact whether and how assistive devices are used. In this talk, I will share and unpack stories from people with various abilities to argue that assistive technology design through the lens of “interdependence” provides a more honest, respectful, and empowering alternative for assistive technology design.
Gabriela: Addressing health inequities through human-centered design
Talk 1 - Abstract: When we use empathy and human-centered approaches in developing health interventions, we have the capacity to affect social change. We can direct human-centered computing toward underserved populations. We can target marginalization, stigma, and inequity with human- centered methods. In this talk, I will share projects that have focused on addressing inequities within children’s behavioral health services, treatment for youth living with HIV, and opioid overdose prevention. I will present methodological approaches to designing for and with underserved populations, and show how to practice inclusion and equity in the design process. Based on the results of my projects, I will also outline design principles for health information technologies that do not sacrifice humanity for standardization. Finally, I will discuss the importance of broadening participation in computing, for more equitable research participation, methods, and output.
This talk discusses three main areas in this research: 1) How well does OSN data reflect real-world population data, 2) What are the patterns in response behavior to these events, and 3) How can low-quality information be filtered out from these data sources?
I will present findings across these questions, showing social media data mirrors certain geographic populations, discussing event-detection algorithms, and outlining some current research in cross-platform information quality. I will then open discussion on future work in: OSN data for qualitative study, crisis informatics, and studies of population/platform differences in online information quality.
Bio: Dr. Cody Buntain is a postdoctoral research fellow at the University of Maryland’s Human-Computer Interaction Lab and is funded by the Intelligence Community Postdoctoral Fellowship. His current areas of research include studying complex social systems and how society leverages social media in the aftermath of crises and social unrest. This research includes evaluating information credibility across social media platforms, real-time information retrieval and event detection in response to crises, social media reflections of real-world phenomena, and the intersection of machine learning and computational social science.
Designing with Data: How machine learning is morphing human, product, and system design
Abstract: The nature of product design has increased in scale, both inside corporations and in self-organized online communities (e.g., OpenIDEO, Local Motors). This is thanks to unprecedented amounts of digital design information made possible by globally distributed groups of thousands of people who collaborate together on design projects over the Internet. However, this increased scale and diversity comes with a price: 1) these groups generate more data than they can effectively use, 2) it becomes difficult to leverage their diverse expertise, and 3) involving non-experts meaningfully the design process, particularly for complex mechanical systems, requires rethinking how people interact with design tools and what kind of intelligent support we need to provide.
Important medical research is increasingly based on analysis of data collected during provision of routine care. Compared to clinical trials data, this "secondary use" data is not susceptible to randomized, prospective study protocols; it suffers from poor quality and extreme "missingness" for observational or retrospective methods; strict privacy and human subjects regulations limit its availability; processing it for analysis is complicated by the diversity of its sources, formats, and the plethora of language and coding systems in which it is recorded; and analyzing it generally requires advanced clinical training and methods for grappling with its extreme multi-variateness, sparsity, and unknown systemic biases. Despite these formidable challenges, this data is orders of magnitude cheaper and more prolific than clinical trial data. Researchers and analysts within medical provider institutions can have access to data for millions of patients essentially for free; while medical products companies, regulators, and payer institutions can affordably purchase data for hundreds of millions of patients. Further, although analysts' uses cases are diverse and their methods (e.g., advanced statistics or machine learning) often opaque as well as immature; they share many basic questions and tasks: they almost universally need to characterize their populations on various demographic and clinical dimensions; they generally need to choose study and comparator cohorts; they need to group patients by disease and treatment parameters; they need to evaluate the significance of untold co-morbidities and confounders; they need to explore and discover temporal patterns obscured by the volume and variability of the data.
The advent of common data models and open-source software is just beginning to drastically streamline research workflows with this data. For analysts with access to data in OHDSI (ohdsi.org) format, for instance, many months of the standard observational study workflow can be skipped entirely. OHDSI's web-based cohort construction tools and it's open and growing R methods library allow researchers not only to define and execute their studies in hours or days rather than months, these researchers can now instantly and precisely share their code and aggregate results in a research network to be immediately replicated on dozens of other databases containing records for hundreds of millions of patients.
What this means for my research is: 1) my visualization tools can be built to a single data model and can be tested with a wide variety of use cases and without requiring my subject matter expert collaborators to perform data collection and transformation just to work with me; and 2) my tools can be built with immediate integration into platforms they are already using, so, for instance, they can take advantage of these experimental visualization tools as they design their study and set parameters; they can feed those parameters into their statistical or machine learning algorithms; and they can then (continuing in the same platform) use these visualization tools to explore and evaluate results.
What it also means for my research, for better or worse, is that my model for developing and evaluating visualization software and working with users and collaborators is very different from what HCI researchers are used to, and, since no one at UMD (as far as I know) is using OHDSI or anything like it, I have been spending more time explaining and evangelizing for my preferred research platform than for my research itself.
At the Brown Bag I will talk about both; but depending on audience interest (most of our visualization researchers will be off at IEEE VIS this week), I may end up focusing more on the infrastructural issues.
Internship Panel? (TBD)
Technologically Mediated Teacher Noticing
Abstract: We introduce technology-mediated teacher noticing (TMTN): a vision for the design and use of technology-mediated tools that takes seriously the need for teachers to attend to, interpret, and respond to their students’ thinking. This vision is situated at the intersection of research on teacher noticing, and on technology to support student thinking. We synthesize that work to highlight specific ways that technology-mediated classroom tools can focus and stabilize teachers’ attention on valuable aspects of student thinking emphasized by current reform efforts. We then illustrate TMTN with classroom examples in which technology supported or obstructed teachers' attention to student thinking, and consider implications for research on technology in teacher practice, professional development, and the design of technological tools for K-12 classrooms.
Karen Holtzblatt and Chris Robeck
|11/23/2017||No Brown Bag, Thanksgiving recess|
Past Brown Bags
View the Past Brown Bag Lunch Schedules to learn more about prior talks.