work portfolio

At Well Played, I collaborated with UXR teams to develop comprehensive qualitative research reports for leading video game companies. My responsibilities included generating detailed figures from large data sets to support and express key insights, and creating topline reports and polished slide decks to present user research to big name clients in the gaming industry. I also designed screening surveys to recruit qualified participants for user playtests.

At Well Played, I collaborated with UXR teams to develop comprehensive qualitative research reports for leading video game companies. My responsibilities included generating detailed figures from large data sets to support and express key insights, and creating topline reports and polished slide decks to present user research to big name clients in the gaming industry. I also designed screening surveys to recruit qualified participants for user playtests.

Over the course of my internship, I independently designed and moderated an internal qualitative research project. I was tasked with testing and analyzing a new method of think-aloud research. A think-aloud study is a research method where participants vocalize their thoughts while interacting with products, akin to a streamer discussing their gameplay without an audience. This approach enables researchers to gain insights into participants unbiased, real-time opinions rather than the opinions they might form after listening to others.

I chose the game Risk of Rain 2 for the study, hosting six paid participants at our Atlanta office. After providing instructions, participants engaged in gameplay followed by a live discussion. Key findings included:

After providing instructions, participants engaged in gameplay, followed by a live discussion. Here are some of the key findings we gained from the study:

• Participants initially started off quiet but became more talkative after an average of 5 minutes.
• As the study progressed, participants grew quieter, with only around 20% continuing to comment regularly after the 35-minute mark.

Solutions identified to address these issues were:

• Starting the study with a group discussion to energize social commentary and create a comfortable atmosphere.
• Implementing regular breaks every 30 minutes to allow participants to talk as a group and re-energize their conversational skills.
• Engaging with participants during gameplay to encourage them to talk, then gradually stepping back to allow them to continue independently.

Additionally, I developed and defined key user metrics to reflect user experiences in playtests and communicated actionable insights to improve user research. My experience at Well Played provided invaluable professional research experience, allowing me to demonstrate leadership, initiative, and responsibility.

Class Connect was a class scheduler developed as part of our research and design work in Human-Computer Interaction.

Class Connect was a class scheduler developed as part of our research and design work in Human-Computer Interaction. Over the semester, my four-person team employed various research, analysis, and design methods to create a solution for a problem space we identified.

Starting with class registration as our focus area, we began with observations and quickly moved to user interviews. I gained experience with long, semi-structured interviews, which helped us create empathy maps and pinpoint several user pain points:

• Registration can be stressful and time-consuming.
• Scheduling advisor meetings can be challenging.
• Finding credit-filling and interest-aligning electives is difficult.
• The current UI is overly complicated and forces students to use third-party apps.

Next, we developed "how might we" statements and points of view to explore potential solutions. Through iterative processes, including sketches, wireflows, and wireframes, we devised a practical solution to address these issues.

We then tested our potential solutions by creating an in-depth paper prototype to help users visualize the process and flow of our design. Participants were asked to complete various tasks using the prototype, and we recorded their pain points, comments, and other feedback to refine the design.

Our semester concluded with the creation of a class scheduler design concept using Figma. We conducted multiple rounds of user testing to perfect the design as much as possible. Click below to see the final product on Figma or access the final report for more details:

Under the guidance of Dr. Adam Goodie and PhD student Theresa Reilly, our team investigated the relationship between point-based incentives and real-money gambling tendencies in video games.

Under the guidance of Dr. Adam Goodie and PhD student Theresa Reilly, our team investigated the relationship between point-based incentives and real-money gambling tendencies in video games. During this two year study, I helped to prepare materials, run participants, and analyze footage and data. Our small team ran over 250 participants through one of three conditions involving the game "Catch 21", a blackjack variant where players try to stack cards to reach 21: Under the guidance of Dr. Adam Goodie and PhD student Theresa Reilly, our research team investigated the relationship between point-based incentives and real-money gambling tendencies in video games. Over the course of this two-year study, I assisted in preparing materials, conducting participant sessions, and analyzing footage and data. Our small team ran over 250 participants through one of three conditions involving the game 'Catch 21', a blackjack variant where players aim to stack cards to reach 21:

• Control Play: Participants play Catch 21 for points on a separate website before transitioning to our main site, where they have the option to play for cash.
• Free Play: Participants play on our main site for points only.
• Cash Play: Participants play on our main site for points and then are given the choice to continue playing for cash.

Quickly completing stacks awarded higher points as players competed against real opponents. Participants competed for points and were later offered a $20 gift card, which they could either bet on the game or keep and continue playing for points.

While the study has yet to be officially published, preliminary data shows that 45.23% of participants chose to gamble the money after initially playing for points. Complete statistics will be available once the study concludes, and I will provide a link here.