Matthew Bernacki discussed his team’s current work to improve undergraduate success in STEM courses. The team uses campus data systems to provide targeted support for self-regulated learning. They track data about course document downloads, calendar views in Sakai, class outline downloads, posting questions in Piazza forums, guided reading question downloads, and other course engagement to understand if a student will be successful in a class. Then they act on this data, providing support to help students engage more productively with their course, improving student success rates. The system is accurate enough to predict end-of-semester success within just a few lessons, allowing plenty of intervention time. Further, it can identify students at risk of underperforming with 70% accuracy.
Bernacki explained that understanding a student’s potential to underperform allows the group to provide peer-driven advice, referrals to the learning center where students can receive professional support, and targeted multimedia on a variety of topics, such as cognitive learning strategies, metacognitive self-regulation, and how to set up successful environments, cues, and habits. Students can then transfer their new skills to their coursework, providing a greater chance of success. Bernacki and his team hope to use this tracking to improve STEM career pathway outcomes and involvement throughout college, improving the volume and diversity of students pursuing science throughout their college and professional career. Currently, the model is being used to track UNC Biology 101 students, but the team hopes to find new collaborations in the future.
Matthew Bernacki, Associate Professor and Donald & Justeen Tarbet Distinguished Scholar
Department: School of Education | Faculty Profile
Featured on: January 26, 2023 (Event Page)
Session Title: Advancing Education, Training, and Care (Event Recap)
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