I'm a 4th year Ph.D. student at Northwestern University in the Technology and Social Behavior (TSB) program advised by Professor Haoqi Zhang and Professor Darren Gergle.
I study networked orchestration: how people access and learn to access the support opportunities available in the complex socio-technical ecosystems (e.g., venues, tools, resource guides, and social structures) found in modern-day work and learning communities. I design networked orchestration technologies that aim to assist members of a community in the process of learning to access support opportunities in a networked community. I hope that these technologies will enhance the ways we work and learn with each other in our organizations and communities. My work draws from the fields of human-computer interaction (HCI), social and crowd computing, and artificial intelligence.
Our prior study of Networked Orchestration in the Design, Technology, and Research (DTR) program revealed the need for tools that help learners develop network access strategies (e.g., how to monitor for needs, identify support opportunities, and plan to use opportunities to resolve needs) to effectively work or learning in networked communities. Effective coaching of these network access strategies requires mentors to (1) monitor for how students are accessing the network to support their needs; and (2) scaffold the practice of new strategies across the different venues they engage students in. However, mentors struggle to maintain awareness of students’ access strategies since these practices occur across multiple interactions with work processes, venues, and tools in the network. Further, mentors have limited awareness of if a student has practiced the strategy they suggested, and the outcome of that attempt.
To overcome these challenges, we introduce networked orchestration scripts, computer programs that support networked communities in developing effective networked access strategies. The core idea behind orchestration scripts is to computationally represent the situations in which to enact networked access strategies that mentors have as programs that can be used to monitor for learning opportunities and facilitate their practice. Composing and executing networked orchestration scripts involves 3 components:
Work and learning communities have become increasingly networked to support their members in developing the skills to solve complex, real-world problems. Though disciplinary knowledge remains important to tackle these problems, working effectively in modern-day communities of practice demands the ability for one to learn how to access networked support (e.g., venues, tools, resource guides, or peers) throughout the socio-technical ecosystem. Against this backdrop, we study networked orchestration--how community members access and learn to access networked supports--in the Design, Technology, and Research (DTR) program for undergraduate research training for undergraduate research training.
Through field observations and in-depth interviews, we find that students in the networked research community dynamically engage with their mentors and peers across multiple venues throughout the week in order to identify, clarify, and resolve their needs. Mentors in the community monitor how students are engaging with the supports available in the network, and provide coaching on effective strategies when students are ineffective on their own. We also identify the challenges involved in each of these processes. For students, these include: (1) not realize the needs they may have; (2) difficulties in identifying potential venues that can provide support; and (3) difficulties in accessing an opportunity even if it is known due to an unfamiliarity with the venue or person. For mentors, these include (1) struggling with students’ access strategies often being invisible to them since they occur in venues the mentor was not present in; and (2) with the difficulty in tracking the many skills that they are working on with each of their students over time.
These insights lead to practical design implications for networked orchestration technologies that require an ecosystem-level view of interactions occurring across the venues and tools in a community in order to support the development of network access skills to work effectively in networked communities. We envision that these networked orchestration technologies will be crucial for fostering the networked ways of working and learning that are becoming prevalent in our workplaces and learning communities.
Participatory sensing systems in which people actively participate in the data collection process must account for both the needs of data contributors and the data collection goals. Existing approaches tend to emphasize one or the other, with opportunistic and directed approaches making opposing tradeoffs between providing convenient opportunities for contributors and collecting high-fidelity data.
Our approach explores a new, hybrid approach, in which collected data--even if low-fidelity initially--can provide useful information to data contributors and inspire further contributions. We realize this approach with 4X, a multi-stage data collection framework that first collects data opportunistically by requesting contributions at specific locations along users’ routes and then uses collected data to direct users to locations of interest to make additional contributions that build data fidelity and coverage.
To study the efficacy of 4X, we developed LES, an iOS application for collecting information about campus locations and events using the 4X framework. Results from two field deployments (N = 95, N = 18) show that the 4X framework created 34% more opportunities for contributing data without increasing disruption, and yielded 49% more data by directing users to locations of interest. These findings demonstrate the value and potential of multi-stage, dynamic data collection processes that draw on multiple sources of motivation for data, and how they can be used to better meet data collection goals as data becomes available while avoiding unnecessary disruption.
Jupyter Notebooks, Pandas, Numpy, Scikit Learn, R