My Research

My work mainly falls under two themes: understanding student learning and developing intelligent educational technology. At the moment, I am not looking to take on anymore students. But if you are interested in these topics and are a top student, you can likely convince me otherwise. 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 Student Learning:
  

  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 [4-6], using analytics to provide insights to learner processes [2,8,9] and program effectiveness [3], and experimenting with new pedagogies to improve learning outcomes [1,7,10,11]. This work typically involves literature review of academic articles and 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 facilitates student learning (theme 2).

  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. X. Chang, B. Wang, and B. Hui. (2022) Towards an Automatic Approach for Assessing Program Competencies.
  4. B. Hui, P. Rajabi, and A. Pinchbeck. (2021) Disparity Between Textbook Examples and What Young Students Find Interesting.
  5. B. Hui. (2020) Lessons from Teaching HCI for a Diverse Student Population.
  6. K. Khademi & B. Hui. (2020) Towards Understanding the HCI Education Landscape.
  7. B. Hui & R. Campbell. (2018) Discrepancy between Learning and Practicing Digital Citizenship.
  8. J. Bulmer, A. Pinchbeck, & B. Hui. (2018) Visualizing Code Patterns in Novice Programmers.
  9. B. Hui & S. Farvolden. (2017) How Can Learning Analytics Improve a Course?
  10. M. Bojey & B. Hui. (2016) Who Wants to Collaborate? A Step Towards Understanding Collaboration as Choice.
  11. M. Bojey, B. Hui, & R. Campbell. Engaging Higher Order Thinking Skills with a Personalized Physics Tutoring System.

• Theme 2: Developing Intelligent Educational Technology:
  

  Over the years, we have developed a number of intelligent systems to support teaching and learning in various contexts. Below is a list of peer-reviewed published work in this area. Some of these papers are connected because they address different aspects of one system. For example, [14-15] argues for the use of an educational tool called the concept map as a way to support self-regulated learning in an intelligent system that incorporates student analytics. This idea was later implemented as part of a learning platform [4] that also provides autograding capabilities [5]. Another line of papers involve the development of an AI algorithm [13] which is used in a team formation system [8]. I also laid out a design framework for this class of software [7] as well as identified the research agenda in this area [6]. Steps were taken to develop adaptive models that support online learning and collaboration [1-2]. A third system was developed to support computational thinking activities for young children [3,11]. Lastly, we also developed a skills portfolio system [12] which is related to the mapping of learning outcomes in higher education programs [9-10]. The data collected from using these systems can be analyzed and used to inform student learning (theme 1).

  1. N. Fan and B. Hui. (2023) Understanding the Data Needs for Developing a Computational Model of Team Dynamics.
  2. B. Hui. (2023) Developing a Generative Model of Team Analytics.
  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. B. Hui. (2022) Design Guidelines and Research Directions for Team Analytics.
  7. B. Hui. (2022) Design Guidelines for a Team Formation and Analytics Software.
  8. 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.
  9. A. Chaudhuri, J. Hirtz, B. Hui, L. Prada. (2022) Workshop on Using Curriculum Map to Promote Diverse and Inclusive Learning Outcomes.
  10. J. Hirtz, L. Prada, A. Chaudhuri, B. Hui. (2022) How to Promote Inclusive and Accessible Practices Using UBC's Curriculum MAP.
  11. B. Hui. (2020/2021) Computational Thinking Activities for Pre-Literate Children.
  12. B. Hui, E. Wood, and C. Khalil. (2021) An Analysis and Evaluation of the Design Space for Online Job Hunting and Recruitment Software.
  13. J. Bulmer, M. Fritter, Y. Gao, & B. Hui. (2020) FASTT: Team Formation using Fair Division.
  14. B. Hui. (2013) A Framework for Self-Regulated Learning of Domain-Specific Concepts.
  15. B. Hui & C. Crompton. (2013) The Need to Support Independent Student-Directed Learning.