Non-Disposable Assignments for Remote Neuroscience Laboratory Teaching Using Analysis of Human Data.

ABSTRACT

To accomplish discovery learning in a remote educational context, while also addressing disparities in laboratory facility/equipment access, instructors can assign Non-Disposable Assignments (NDA) whereby students design research projects, extract data from public sources, analyze data in a cloud-based environment, and share potentially original findings. Unlike typical course assignments (e.g., lab-reports, tests) that remain in the student-teacher dyad, NDAs (e.g., disseminated presentations, visualizations, manuscripts) are associated with enhanced learning and facilitate the integration of diverse student perspectives in the creation, analysis and dissemination of neuroscience. Illustrating the design of a project-based approach to teaching neuroscience laboratory courses, we provide two example NDAs using neural imaging and physiological information available from public databases. We provide a data set in a directly usable form for teaching with R, and present an overview of two user-friendly tools, RStudio and R-Markdown, for remote teaching and learning through data analysis.