American Society of Engineering Education - North Central Section Spring Conference 2018

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Introduction to Neural Engineering: Design and Development of a BME-in-Practice Course through the BME Instructional Incubator

The Biomedical Engineering (BME) Instructional Incubator (Incubator) is a department-level collaborative effort bringing together BME faculty, students (undergraduate and graduate), and postdocs to create hands-on student learning experiences responsive to the rapidly changing field of BME. The Incubator engages students and faculty in the instructional design process and utilizes student learning theory to develop curricula that can be implemented in 1-credit sophomore level BME-in-Practice courses.

One such course is Introduction to Neural Engineering. This particular course was designed to provide students with a broad overview of neural engineering as a discipline. As the name implies, Introduction to Neural Engineering is an introductory learning experience intended for students interested in the brain, particularly the intersection of technology and the nervous system, as well as those interested in developing technical skills related to programming and computational modeling.

This course was developed with BME students first-and-foremost in mind. Through interviews with current and former students and various BME stakeholders (e.g., medical device companies, graduate and medical schools), it was determined that the course should provide value to students by teaching them a variety of tradable skills relevant to work performed in a realistic research setting, such as reading and interpretation of research articles, neuron modeling, signal processing, MATLAB programming, and COMSOL finite element modeling.

What sets Introduction to Neural Engineering apart from other courses in the BME curriculum is its firm grounding in constructivism and situated learning. Inquiry- and experiential learning-informed problem sets scaffold student teams during facilitated lab sessions to enable them to learn alongside each other and the rest of the class. Active pedagogical practices of engagement also influence in-class activities. Ample time is allocated to provide students with locus of control opportunities, where they are able to tinker with algorithms that process neural data sets in meaningful ways. Students are able to construct knowledge during lecture sessions by solving and troubleshooting complex problems using real neural data (acquired from monkeys and humans), applying models to clinical practice, and navigating ethical quandaries specific to neural engineering. Students are encouraged and expected to develop cooperative learning skills and attitudes through extensive group work during lab sessions. Course effectiveness will be evaluated throughout the term through evaluation reports, group reflections, and pre- and post-course surveys.

As the field of neural engineering continues to grow in scientific output, media representation, and social popularity, early student exposure to an academic path capable of leading them to a career in this area becomes even more important. By the end of Introduction to Neural Engineering, students will be able to identify research and work opportunities available in the field as well as be comfortable with constructing basic neural engineering experiments using fundamental neural engineering techniques. These outcomes will help students determine if they wish to pursue a neural engineering curricular focus and potential career.

Karlo Malaga
Department of Biomedical Engineering, University of Michigan
United States

Chrono Nu
Department of Biomedical Engineering, University of Michigan
United States

Aileen Huang-Saad
Department of Biomedical Engineering, University of Michigan
United States


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