Current Student Opportunities

My work mainly falls under two themes: (1) understanding student learning in the context of novice programming and (2) in team programming contexts. At the moment, I am looking for students who are interested in data analysis and researching on existing team studies. Opportunities include supervising students in a Directed Studies course (3 credits or 6 credits) and/or an Honours thesis (6 credits, with minimum GPA requirement).

When you reach out to me, please also let me know what your team collaboration experience is and provide your transcript as well as your Github link. You can also take a look at the list of past Directed Studies and Honours theses that I supervised to get a sense of what others did.

Theme 1: Understanding How Students Learn and Supporting Their Learning Process:
I have conducted a variety of activities to better understand how students learn. These activities include literature reviews and comparisons to situate our teaching context [7-9,11,19], using analytics to provide insights to learner processes [4,5,18] and program effectiveness [12-14], and experimenting with new pedagogies to improve learning outcomes [6,8,11,18,19]. The studies are usually situated in a learning theory (such as self-regulated learning [1-2,16] and self-determination theory [10]) and implemented in an educational technology in order to gather data [3,7,9,12-13,15-17].

This work typically involves understanding academic articles and conducting data analysis using a mix of qualitative (e.g., thematic analysis, coding, intercoder reliability) and quantitative methods (e.g., hypothesis testing). Investigating into student learning helps me understand what students need to improve their learning processes and learning outcomes. In some cases, this knowledge motivates me to develop intelligent (AI-assisted) educational technology that provides new opportunities for teaching and learning.
1. B. Hui. (2023) A Personalized Learning Approach to Support Students with Diverse Academic Backgrounds.
2. B. Hui. (2023) Are They Learning or Guessing? Investigating Trial-and-Error Behavior with Limited Test Attempts.
3. O. Adeyemi and B. Hui. (2023) Using Open Technology to Bring Computational Thinking Activities to the Outdoors.
4. K. Khademi, M. de Vin, C. Ricca, A. Adiraju, L. Lin, O. Adeyemi, and B. Hui. (2023) An Open CS1 Learning Platform to Promote and Incentivize Deliberate Practice.
5. O. Adeyemi, A. Adiraju, S. Akins, K. Khademi, and B. Hui. (2023) JUnit++: An Open Educational Tool for Simplifying Unit Testing.
6. X. Chang, B. Wang, and B. Hui. (2022) Towards an Automatic Approach for Assessing Program Competencies.
7. A. Chaudhuri, J. Hirtz, B. Hui, L. Prada. (2022) Workshop on Using Curriculum Map to Promote Diverse and Inclusive Learning Outcomes.
8. J. Hirtz, L. Prada, A. Chaudhuri, B. Hui. (2022) How to Promote Inclusive and Accessible Practices Using UBC's Curriculum MAP.
9. B. Hui, E. Wood, and C. Khalil. (2021) An Analysis and Evaluation of the Design Space for Online Job Hunting and Recruitment Software.
10. B. Hui, P. Rajabi, and A. Pinchbeck. (2021) Disparity Between Textbook Examples and What Young Students Find Interesting.
11. B. Hui. (2020/2021) Computational Thinking Activities for Pre-Literate Children.
12. B. Hui. (2020) Lessons from Teaching HCI for a Diverse Student Population.
13. K. Khademi and B. Hui. (2020) Towards Understanding the HCI Education Landscape.
14. B. Hui and R. Campbell. (2018) Discrepancy between Learning and Practicing Digital Citizenship.
15. J. Bulmer, A. Pinchbeck, and B. Hui. (2018) Visualizing Code Patterns in Novice Programmers.
16. B. Hui and S. Farvolden. (2017) How Can Learning Analytics Improve a Course?
17. M. Bojey, B. Hui, and R. Campbell. Engaging Higher Order Thinking Skills with a Personalized Physics Tutoring System.
18. B. Hui and C. Crompton. (2013) The Need to Support Independent Student-Directed Learning.
19. B. Hui. (2013) A Framework for Self-Regulated Learning of Domain-Specific Concepts.
Theme 2: Investigating Team Formation and Collaboration Dynamics:
My work in understanding student learning expanded to team contexts, and I began studying how teams should be formed as well as how we an best support work done in student teams. Below is a list of peer-reviewed published work in this area. Initially, we looked the relationship between self-efficacy and choosing to work in teams as well as the impact that teamwork has on efficiency [1]. Soon after, as class sizes began to increase, I began working on a team formation algorithm that helps instructors form teams strategically and automatically [2]. The tool is now called Teamable Analytics [3] and has been used widely because it covers a lot of different use cases [4-5]. Forming teams is just the beginning; helping students learn to work effectively in teams is an ongoing challenge. To date, this is an open research problem and we have began tackling it in the context of monitoring student teams in software engineering projects [6-7].

This work focuses on developing new algorithms, developing new computational models, and conducting simulation experiments.
1. N. Fan and B. Hui. (2023) Understanding the Data Needs for Developing a Computational Model of Team Dynamics.
2. N. Fan and B. Hui. (2023) Developing a Generative Model of Team Analytics.
3. B. Hui. (2022) Design Guidelines and Research Directions for Team Analytics.
4. B. Hui. (2022) Design Guidelines for a Team Formation and Analytics Software.
5. B. Hui, O. Adeyemi, M. De Vin, B. Marshinew, K. Khademi, J. Bulmer, and C. Takasaka. (2022) Teamable Analytics: A Team Formation and Analytics Tool.
6. J. Bulmer, M. Fritter, Y. Gao, and B. Hui. (2020) FASTT: Team Formation using Fair Division.
7. M. Bojey and B. Hui. (2016) Who Wants to Collaborate? A Step Towards Understanding Collaboration as Choice.