Hybrid Modular Courses

Using Hybrid Modular Courses to Scale Up Engaged Learning

Faculty in the School of Kinesiology are highly motivated to improve their students’ critical thinking skills, create opportunities for students to engage with meaningful questions and solve problems with their peers, and work with students in the classroom in the same way that they work with students in their research laboratories, that is, side by side. The ability of faculty to offer more engaged-learning, action-based experiences has been constrained by traditional course design and by the need to deliver the curriculum to ever-increasing numbers of students. We envision a new design – hybrid modular courses – as a potential solution to the competing demands of providing low enrollment, active-learning courses while delivering a cohesive curriculum to many students.

The hybrid modular courses will be clustered around common learning themes related to critical thinking and inquiry/analysis skills. The courses will be hybrid because didactic components will be offered online and classroom components will focus on engaged, active-learning experiences. The courses are modular because they will be offered for reduced credit (2 credits) and can be stacked as multiple experiences for students during full or half terms. The project will test the hypothesis that hybrid modular courses can transform teaching and learning practice by engaging more students in action-based learning while increasing curricular efficiency. If successful, an important outcome of the project will be a curricular framework that could be applied to other programs across campus.

Project Team:

Melissa Gross, Associate Professor of Movement Science, School of Kinesiology

Steven Broglio, Associate Professor of Kinesiology, School of Kinesiology

Leah Robinson, Associate Professor of Kinesiology, School of Kinesiology

Peter Bodary, Clinical Assistant Professor of Kinesiology, School of Kinesiology

Deanna Gates, Assistant Professor of Kinesiology, School of Kinesiology