How Can Technology Improve Student Collaboration in Computer Science? An Interview with Bowen Hui

An interview with Bowen Hui, recipient of the 2026 Mary Kenneth Keller Computer Science & Engineering Undergraduate Teaching Award.

Bowen Hui is an Associate Professor of Teaching in Computer Science at the University of British Columbia Okanagan, whose research into technology-enhanced learning and human-computer interaction has pioneered new models for equitable collaboration and student-centered pedagogy.

We connected with Dr. Hui to discuss the integration of classroom data into research, the nuances of algorithmic team formation, and how "time-boxed" mastery learning can reshape student outcomes in technical disciplines.

Your research has spanned many different topics across HCI and computing solutions to group dynamics/work. Can you describe your journey as a researcher and educator, and how you found the area you decided to concentrate on?

I’ve always been drawn to interdisciplinary research that explores how people think and collaborate, particularly how technology shapes human interaction and group work. When I began teaching at UBC, my research naturally evolved as my teaching and research began to inform each other. This led me to focus on technology-enhanced learning in computing education, specifically how students’ learning behaviours change in technology-rich environments and how we can design tools and learning experiences that better support their learning and collaboration.

Given your solid foundation in pedagogy, what advice would you give to a PhD student who loves education but feels pressured to follow a traditional research-intensive path?

Education can be incredibly rewarding because the impact on students is often direct and immediate, whereas research impact typically takes longer to be felt. For PhD students who love education but feel pressure to follow a traditional research-intensive path, my advice is to remember that academic careers are becoming more diverse. Many institutions now offer teaching-focused postdocs and faculty roles that value pedagogical scholarship and educational innovation. Exploring these paths can allow them to build a career that aligns with what motivates them while still contributing meaningfully to academia.

How would you describe the unique impact and day-to-day fulfillment of the educational work you do, and how it can coexist with rather than interfere with research?

My research is directly motivated by what I see in the classroom—whether that’s students struggling with a type of problem, challenges in group work, or questions about assessment. I design new activities, tools, and assessments, and then use learning theory and data to study how these interventions affect student learning. This work has immediate benefits for students while also contributing to our broader understanding of learning and behaviour. In this way, my classroom functions as a living research environment, where teaching and research are fully integrated rather than competing priorities.

You have supervised 128 Directed Studies projects and Honours theses in 13 years, which is an incredible volume alongside a full teaching load. What is your "system" for providing high-quality, personalized mentorship to so many students without experiencing burnout?

I genuinely enjoy working with students who pursue Directed Studies and Honours theses because they’re highly motivated, which makes the mentorship very rewarding. To make this sustainable, I intentionally structure supervision as small project groups based on overlapping interests. This keeps me focused within the same research space while still supporting individual needs, and it creates a peer learning environment where students feel more comfortable sharing progress and challenges. Whenever possible, I begin working with students in their third year or earlier so they can continue in subsequent semesters. This allows them to build depth and ownership by contributing to different aspects of a longer-term project over time.

Your research explores algorithms for balanced team formation with a focus on gender dynamics. What are the common "invisible" pitfalls in student group work that these algorithms are designed to solve to ensure equitable collaboration?

Our algorithms and tools are designed to be flexible so instructors can decide what matters most to them when forming teams. One key issue we address is tokenization. Our approach allows students to self-identify minority status, such as gender, so teams can be formed in ways that avoid placing a single minority student in isolation. Research shows that microaggressions and participation barriers are often amplified when students are the only representative of a group, so preventing tokenization can make collaboration more equitable. We also incorporate factors like allowing students to request a friend in their group, which can increase psychological safety and help students feel more comfortable contributing. Together, these design choices address many of the subtle barriers that can otherwise go unnoticed in group work.

You’ve established a faculty-wide peer-mentoring program. For an undergraduate student who feels like an "outsider" in Computer Science, what is the most effective way a peer mentor can help them build a lasting sense of belonging?

Building community is the most important factor for students who feel like outsiders. In the program, we structure conversations around the challenges students face at different points in the year, especially those moments when they don’t know where to turn for help. A strong peer community gives them trusted people to ask for advice and helps them navigate resources and faculty more confidently.

We’ve found that the most powerful mentoring happens when mentors share their own experiences and how they overcame challenges. Hearing that someone “like them” faced similar struggles and succeeded helps normalize those feelings and creates relatable role models, which is key to building a lasting sense of belonging.

How do you restructure a traditional computer science course (which often has rigid technical requirements) to allow students to shape the experience around their individual goals and take ownership of their education?

Where possible, I use project-based learning to help students connect course material to their own goals and take greater ownership of their education. This approach gives them room to be more creative and invested in what they produce, while still working within core technical requirements. I also intentionally design courses with open-ended questions and reflective checkpoints so students can think about their learning trajectory and how the material connects to their interests. For example, I have interviewed graduating students and created videos of their projects and shown those videos in first-year courses and outreach settings.

These examples help students see what is possible and often spark ideas about how they can shape their own learning in ways that align with their personal and career aspirations.

What would you consider the biggest misconception that the general public (and parents) has about what it means to "teach a child to code”?

I think the biggest misconception is that teaching a child to code is the same as teaching computer science. Programming is important for turning ideas into prototypes, but it’s really just a tool. Computer science is fundamentally about problem-solving, creativity, collaboration, and understanding how technology can shape society.

There’s also a common belief that this path is mainly about making money in the tech industry. In reality, computing can be used for social good in many ways, such as promoting healthy technology use and helping people manage their finances. Teaching children to code is really about empowering them to create and think critically about the digital world around them.

Group work is integral to education, but also oftentimes the least liked. Your research addresses equitable collaboration; how will/has your research help improve student outcomes on a large scale and across age groups?

My work focuses on designing group formation and assessment practices that make collaboration fair and motivating. I prioritize factors like shared goal orientation and existing friendships to encourage positive team dynamics, and I build strong individual accountability into assessments so a student’s grade is not overly affected by uneven contributions—one of the biggest concerns students have about group work.

I also collect regular feedback and intentionally build connections with students so I can identify group challenges early and intervene when needed. With over 100 students in my classes, it usually takes me a few weeks to learn everyone’s names. But once I accomplish that, I generally know their group dynamics and individual tendancies, which enables me to detect potential conflicts earlier rather than later. To increase engagement and learning at scale, I incorporate structured peer review and project showcases, where students learn from each other’s work and contribute to community recognition through “People’s Choice” awards.

Together, these practices help make group work more equitable, engaging, and scalable across different class sizes and age groups.

Beyond technical skills, what is the most important "soft skill" students gain from a research assistantship that makes them more competitive in the job market?

I would highlight oral communication as one of the most valuable soft skills students gain. Many students begin with strong technical abilities but limited confidence or experience in articulating their ideas. Through research assistantships, they regularly explain their work, justify decisions, discuss challenges, and brainstorm solutions with me and their peers. Over time, they learn to think on their feet, listen actively, give and receive constructive feedback, and clearly communicate complex ideas to people who may not share the same mental model as them. Learning how to report back on their explorations and assessment of alternative approaches to obstacles when they are stuck is crucial to success in the workplace. These skills are incredibly transferable and make them much more competitive in the job market.

Winning the CSCan/InfoCan Excellence in Teaching Award is a major milestone. What do you believe is the most "innovative" change you’ve made to the CS curriculum at UBC Okanagan that contributed to this national recognition?

The most innovative change I’ve introduced is what I call time-boxed implementation for mastery learning. Instead of unlimited assessment attempts, I break up exams into low-stake assessments for each module and align them to a window of three weeks at three spaced intervals, allowing students multiple opportunities to demonstrate understanding without penalty. At the same time, students can move ahead of the schedule if they wish, giving them flexibility to align learning with other course demands and personal commitments.

I’ve implemented this in both multiple-choice and mixed open-ended formats in a way that remains manageable at scale. An interesting outcome is that students don’t just aim for full marks. My data analysis revealed that students often continue making attempts even after answering correctly, suggesting they are using the process to deepen their understanding. Overall, the approach has been well received because it supports both structure and flexibility while promoting genuine learning.