T-17: Collaborative Teaching for Student Success

Blended learning fosters active engagement, autonomy, and flexibility by integrating online and face-to-face instruction. A community of practice of five CEGEP teachers from physics, mathematics, biology, and social sciences explored its impact through focus groups and interviews. Benefits included improved performance and time management, while challenges varied by discipline—physics and biology benefited from simulations, mathematics required structured guidance, and social sciences needed peer discussions. This presentation, aligning with the conference theme, reflects on crafting discipline-specific blended learning strategies to enhance student success, peer collaboration, and academic integrity in higher education.

This talk will explore optimal group formation for educators utilizing group projects to enhance student learning. Students in a large first-year undergraduate Computer Science course completed an assessment to quantify their Big Five personality traits. The assessment results were used to form groups using a Minimum Entropy Collaborative Grouping (MECG) algorithm and GPT-4o, both aimed at creating heterogeneous groups. The talk will compare the efficacy of heterogeneous grouping versus randomized grouping, and groups formed with MECG versus GPT-4o. This talk will offer new perspectives and tools to support student success by optimizing group formation to the SALTISE community.

Our research group has been in a successful collaboration with instructors from undergraduate STEM courses for multiple years. At the heart of this fruitful partnership is a mutual dedication to advancing each other’s missions of research and practice. While instructors help us collect data for our studies investigating student motivation, we share personalized research insights to help improve their teaching approach. Here, we present our research dissemination efforts and how we are refining them based on instructor-driven insights gained from previous iterations. Our aim is to offer a model that others can use to strengthen and sustain their researcher-instructor collaborations.