Scaling Active and Experiential Learning in Courses through the Syndicate Teaching Model

Project Lead: Fatemeh Salehian Kia, School of Information

Project description

This project pilots a syndicate teaching model to address the limitations of lecture-based teaching in large computational courses. The model combines shorter lectures to introduce concepts with small-group syndicate sessions, fostering active learning, problem-solving, and critical thinking. Each syndicate group, led by teaching assistants, engages 20–25 students in guided, interactive exercises supported by PrairieLearn, an adaptive platform offering auto-graded tasks, immediate feedback, and performance analytics. Implemented in three courses—Computer Programming for Information Professionals, Database Design, and Human-Centered AI—this scalable approach enhances student engagement and learning outcomes. The project will evaluate and refine the model for broader implementation at UBC.

Research questions

  1. How does the integration of syndicate teaching, facilitated by PrairieLearn, impact students’ behavioral engagement (e.g., participation rates, task completion) and cognitive engagement (e.g., attempts at higher-order questions, time spent on complex tasks, progression through challenges) in the pilot courses?
  2. How effective is PrairieLearn in scaling the syndicate teaching approach by providing immediate feedback, facilitating hands-on exercises, and supporting large-scale instruction through features like auto-grading, analytics, and content adaptability?
  3. What are students’ perceptions of the syndicate teaching model, and how do these perceptions align with their engagement and learning outcomes?