Mind the Gap: Addressing "Missing Pieces" in Neuroscience Curricula to Reinforce Research Rigor

摘要 哈佛大学的神经科学博士课程一直是神经科学发展的领导者。 创新的研究严谨性培训课程，从2011年6月创建的 课程“生物学家的定量方法”，由Michael Springer和Richard Born博士领导。由于这 与此同时，我们继续开发这一领域的课程，包括研究严谨性的具体指导 和再现性从统计的角度在课程“思考数据”，由博士. 我们建议通过开发教学材料来扩大和加强这些努力， 神经科学课程中的“缺失部分”，以加强研究的严谨性：1）识别和消除 实验设计和分析中的偏差来源，2）区分实验失败和 生物学答案; 3）调查分析的细节和假设; 4）进行编码 分析重现性。我们的教学方法将强调主动学习和嵌入 在特定的神经科学背景下的概念，以提高学生的参与，学习， 潴留所有单元都将采访不同的科学家，以突出主题的重要性 和不同难度的教程，侧重于具体的神经科学的例子。这些教程将包括 的动画短片，介绍关键思想，交互式可视化，讨论问题，真实的 数据时，适当的，和可选的编码练习，并将在不同的难度水平 并假设专业知识。所有单元都将包括一个“学习者资源页”，其中有摘要说明， 术语表，并指向有关该主题的更多资源的指针，以及一个教师资源页面， 示例教案、评估问题库和可下载内容。

ABSTRACT The PhD Program in Neuroscience at Harvard University has been a leader in the development of innovative curricula for training in research rigor, beginning with the creation, in June 2011, of the course "Quantitative Methods for Biologists," led by Drs. Michael Springer and Richard Born. Since this time, we have continued to develop curricula in this area, including specific instruction in research rigor and reproducibility from a statistical perspective in the course "Thinking about data," taught by Dr. Born. We propose to extend and enhance these efforts by developing teaching materials that will address "missing pieces" in neuroscience curricula to reinforce research rigor: 1) identifying and eliminating sources of bias in experimental design and analysis, 2) distinguishing experimental failures from biological answers; 3) investigating the details and assumptions of analyses and 4) making coded analyses reproducible. Our pedagogical approach will emphasize active learning and embedding concepts within a specific neuroscience context to enhance student engagement, learning, and retention. All units will feature interviews with diverse scientists to highlight the importance of the topic and tutorials of varying difficulty focused on specific neuroscience examples. These tutorials will consist of short animated videos to introduce key ideas, interactive visualizations, discussion questions, real data when appropriate, and optional coding exercises, and will be available in various levels of difficulty and assumed expertise. All units will include a “learner resources page” with summary notes, a glossary, and pointers to further resources on the topic, as well as an instructor resources page with sample lesson plans, assessment question banks, and downloadable content.