“Matching Up Adults in Work Groups” – Exploratory Survey Research for Spring 2020 Class Project

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.

Figure 1: The number of weak ties in a group is negatively associated with top-percentile project outcomes.

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.

Figure 2: The “negative emotionality” personality trait is positively associated with top-percentile project outcomes AND satisfaction.

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.

“Date Assist” Plug-in for Self-Affirmation in Online Dating Apps – Fall 2019 Class Project

Yes I am STILL taking courses towards my doctorate in human-computer interaction (HCI) here at Carnegie Mellon! It’s annoying that my department does not count my 2017 master’s degree in HCI from Indiana University as an acceptable credential. The flip side, though, is that I have experience with group projects, so these feel like a breeze – plus, I get to meet and work directly with PhD students outside my area and with our amazing master’s degree students!

The following is a blog post that we created for our final project in Persuasive Design in HCI, taught by Geoff Kaufman. My teammates are Aaron Bishop, Brandon Fiksel, Samantha Reig, Bidisha Roy, and Molly Schaefer.

A summary image of our "Date Assist" plugin. The QR code links to the interactive Figma prototype.
A summary image of our “Date Assist” plugin. The QR code links to the interactive Figma prototype.

Having anxiety about meeting the right romantic partner in today’s online dating apps? You’re not alone. A Google search of the phrase “online dating is stressful” yielded close to 10 million results! Moreover, our research shows that young adults in a college setting struggle with three issues with dating: finding a mutually agreeable time for the first date, bolstering their mental state before the date, and coming up with conversation starters during the date. 

Enter “Date Assist.” Our plugin for online dating apps uses a touch-screen calendar interface to help you and that special someone easily find a meeting time that works for both. Once the date is scheduled, we help you remember what’s most important to you with a simple quiz about your values. We also help you to build a personalized, meaningful playlist to get you feeling great before your date — and, if you so choose, to share one of your favorite songs to break the ice and ease conversation. Finally, we push out reminders to you that help you to stay positive and remember: you’re a catch!

The psychological mechanism that our “Date Assist” plugin uses is called self-affirmation. In this process (Steele, 1988), a person reflects on valued aspects of the self, allowing those positive aspects to counter the negative effects of seeing other aspects of the self as negative. Such a reflection in one domain can reduce threats in unrelated domains by shifting from a narrow focus on the immediate threat to an expanded perspective of one’s self-worth. 

Our “Date Assist” plugin was developed in Sept.-Dec. 2019 using an iterative, user-centered research and design process:

  • We identified the need to test a self-affirmation in a context that is a threat to an individual’s positive self-concept. 
  • We found a novel research domain for this threat — first dates — that is relevant to the research population that we have immediate access to, young adults. 
  • We used interviews to define this population’s need for help in finding a mutually agreeable time for the first date, in bolstering their mental state before the date, and in coming up with conversation starters during the date. 
  • We created sketches and gathered user feedback. We then consolidated these into a high-fidelity prototype with the Figma collaborative design tool. 
  • To test the effectiveness of “Date Assist,” we propose a large-scale 4×2 experimental study.
Our team met weekly to discuss our ideas, interpret interview data and share low-fidelity sketches. Three images: One of team members Bidisha Roy and Aaron Bishop with others reflected in the lab mirror; a sketch of possible app screens; a menu design in progress.
Our team met weekly to discuss our ideas, interpret interview data and share low-fidelity sketches.

Our process gives us confidence that “Date Assist” can make two contributions to the fields of psychology and human-computer interaction. First, our research extends the existing literature on self-affirmation to the context of online dating. Second, our research provides a novel operationalization of self-affirmation with the creation of the “Date Assist” plugin. 

Most importantly, our work may significantly improve the experiences of those who seek romantic connections via dating apps. We hope that “Date Assist” helps to ease the stressful process of finding love and companionship for brave first-daters everywhere!

‘A Self-Report Measure of End-User Security Attitudes (SA-6)’: New Paper

This month is a personal milestone – my FIRST first-author usability research paper is being published in the Proceedings of the Fifteenth USENIX Symposium on Usable Privacy and Security (SOUPS 2019).

I will present on Monday, Aug. 12, in Santa Clara, Calif., USA, about my creation of the SA-6 psychometric scale. This six-item scale is a lightweight tool for quantifying and comparing people’s attitudes about using expert-recommended security measures. (Examples of these include enabling two-factor authentication, going the extra mile to create longer passwords that are unique to each account, and taking care to update software and mobile apps as soon as these patches are available.)

The scale itself is reproduced below (download the PDF at https://socialcybersecurity.org/sa6.html ):

  • Generally, I diligently follow a routine about security practices.
  • I always pay attention to experts’ advice about the steps I need to take to keep my online data and accounts safe. 
  • I am extremely knowledgeable about all the steps needed to keep my online data and accounts safe. 
  • I am extremely motivated to take all the steps needed to keep my online data and accounts safe.
  • I often am interested in articles about security threats. 
  • I seek out opportunities to learn about security measures that are relevant to me.

Response set: 1=Strongly disagree, 2=Somewhat disagree, 3=Neither disagree nor agree, 4=Somewhat agree, 5=Strongly disagree. Score by taking the average of all six responses.

If you are a researcher who can make use of this work, please download our full research paper and cite us as follows: Cori Faklaris, Laura Dabbish and Jason I. Hong. 2019. A Self-Report Measure of End-User Security Attitudes (SA-6). In Proceedings of the Fifteenth Symposium on Usable Privacy and Security (SOUPS 2019). USENIX Association, Berkeley, CA, USA. DOI: 10.13140/RG.2.2.29840.05125/3.

Many thanks to everyone who helped me develop and bring this project in for a landing, particularly Laura and Jason, Geoff Kaufman, Maria Tomprou, Sauvik Das, Sam Reig, Vikram Kamath Cannanure, Michael Eagle, and the members of the Connected Experience and CHIMPS labs at Carnegie Mellon University’s Human-Computer Interaction Institute. Funding for our Social Cybersecurity project is provided by the U.S. National Science Foundation under grant no. CNS-1704087.