One upside of video calls during the COVID-19 pandemic has been that I can attend or speak at virtually any location or event, without having to travel or move my schedule around too much. It’s helped me get more comfortable with public speaking, and exposed me to different audiences for my work.
In my latest public appearance: I appeared this spring with fellow CMU grad student Tom Magelinski at Bytes of Good Live, organized by Hack4Impact, a student-run nonprofit that promotes software for social good. We talked about our Social Cybersecurity research and what we know of careers in cybersecurity. The recording is available on YouTube, or click on the preview shown below to go to the video. Let me know what you think!
It has been a joy and fascination to help pilot and design research into a very different manifestation of internet-enhanced life than the one I know in the U.S., directed by lead author Hong Shen (also a graduate of the University of Illinois College of Media) and with fellow HCII Phd researcher Haojian Jin and my awesome advisors, Laura Dabbish and Jason Hong. In China, you don’t have to go out with your wallet, just your phone! Even street vendors have QR codes for you to scan! Which gives rise to new forms of communication, such as attaching a message with a transfer equal to a penny! and new threat models, such as thieves coming in the night and replacing the QR code printout with their own!
And that was just from the pilot interviews. Read the preprint version of the paper for specifics on what my Chinese co-authors discovered when they conducted a survey (n=466) and interviews (n=12) in China about the advantages and the pitfalls of moving to a largely mobile and cashless economy.
I spoke up about my interest in the project in part thanks to Dan Grover, whose blogging (in English, thankfully 🙂 ) about his experience of working at WeChat as a product manager had piqued my interest in the various advances in the Chinese social media ecosystem. I couldn’t agree with him more in his tweeted responses to the EO on Thursday night:
If you’re a technologist and you still cling to the quixotic belief that we have a power to change the world with what we do, you have no excuse to let petty politics and nationalism stop you from learning from the best on every side of every divide.
One advantage of still (STILL) taking courses toward my Phd is that I can leverage our group projects to explore research questions outside of my core area. This one got a little “meta” – we looked for the factors that are key to students creating groups for successful course projects!
The following is a blog post that we created for our final project in Social Web in HCI, taught by Geoff Kaufman and Hiro Shirado. My teammates are Ruiqi Hu and Endong Zhu.
In current society, collaboration is a vital component of daily life. People collaborate for diverse personal purposes such as romantic dating, pursuing shared interests, addressing community issues, and solving technical problems. This has led to the rise of dozens of computational systems for “social matching” (Terveen & McDonald, 2005). The rise of team-oriented productivity structures in academia and industry has similarly motivated work to create tools for professional social matching (Olsson et al., 2020). While socio-technical research has led to useful solutions for instructors matching up students in group projects – such as CATME (https://info.catme.org/) and Pair Research (http://pairresearch.io/) – we seek to create a computational tool for students who want to self-organize their project groups.
To help us better envision what such a tool might need for its data inputs, we undertook an exploratory research project in Spring 2020 for the Social Web course in the Human-Computer Interaction Institute at Carnegie Mellon University. First, we undertook a literature search through Google Scholar and our existing reference libraries, and we interviewed subject matter experts and gathered feedback from classmates on what competitor tools exist and what other published research was relevant. From this process, we identified several key variables such as team size, fraction of newcomers and incumbents, team skills, and personality traits, from which to create a statistical model of which input variables mattered most for the desired outcomes of excellent grades and group-work satisfaction.
Then, we designed a pilot survey to help us explore these variables with a real-world dataset. We crafted a codebook of survey items corresponding to these variables, from which we then designed and wrote an online questionnaire in Google Forms. We then recruited survey respondents from among our class and personal networks, and we cleaned and prepared the resulting data for statistical analysis using multilinear regression. Finally, we produced charts and graphs to visualize the most important inputs for determining our respondents’ stated grade and satisfaction outcomes.
Our results showed, first, that the more “weak ties” or acquaintances that were reported in the group, the lower were the project outcomes. We theorize that this is because working with acquaintances will lower people’s expectations for the project – students may just want to “hang out” with their school friends instead of focusing on the quality of their projects.
Second, our results show that the personality trait of “negative emotionality,” such as a tendency to anxiety, is positively associated with both project outcomes AND satisfaction. This finding is surprising to us, because we assumed that this trait would have negative effects on outcomes due to creating psychological obstacles or group friction. However, it may be that students who worry more tend to care more and devote more efforts to the project.
This work has validated our initial hunch that using a psychometric and skills-profiling tool may help students to self-assemble a group for their course projects that is more likely to lead to excellent grades and high satisfaction. We see the need in the future to collect a larger survey sample, with a monetary incentive for participation rather than “social capital” among the convenience sample, in order to test whether we can replicate the results.