Seraphin Sally B, Stock Shannon
Neuroscience Program, Trinity College, 300 Summit Street, Hartford, CT 06106.
Mathematics and Computer Science, College of the Holy Cross, 1 College Street, Worcester, MA 01610.
J Undergrad Neurosci Educ. 2020 Dec 31;19(1):A105-A112. eCollection 2020 Fall.
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.
为了在远程教育环境中实现发现式学习，同时解决实验室设施/设备获取方面的差异问题，教师可以布置非一次性作业（NDA），让学生设计研究项目、从公共来源提取数据、在基于云的环境中分析数据，并分享潜在的原创发现。与保留在师生二元关系中的典型课程作业（如实验报告、测试）不同，非一次性作业（如传播演示文稿、可视化、手稿）与增强学习相关联，并有助于在神经科学的创建、分析和传播中整合不同的学生观点。为了说明基于项目的神经科学实验室课程教学方法的设计，我们使用从公共数据库中获取的神经成像和生理信息提供了两个非一次性作业示例。我们以直接可用的形式提供一个数据集用于R教学，并概述了两个用户友好的工具RStudio和R-Markdown，用于通过数据分析进行远程教学和学习。
