论文标题
数据科学中的近点指导：相互增长的情节
Near-Peer Mentoring in Data Science: A Plot for Mutual Growth
论文作者
论文摘要
大学一直在扩大本科数据科学计划。让研究生参与这些新机会可以促进他们作为数据科学教育者的成长。我们描述了两个采用近乎共同指导结构的计划，研究生指导本科生，以（1）增强他们的教学和指导能力，以及（2）为来自不同背景的本科生提供研究和学习经验。在社会良好计划的数据科学中，本科参与者在团队中工作，以解决具有社会影响的数据科学项目。研究生导师指导项目工作，并提供及时的教学和反馈。 Stanford的数据科学课程指导提供了有效和包容性指导策略的培训。在一个体验式学习框架中，入学的研究生与来自非R1学校的本科生配对，他们通过每周一对一的远程会议指导他们。在计划末期调查中，导师通过这两个计划报告了增长。从这些经验中，我们开发了一份自定进度的导师培训指南，该指南具有教学，指导和项目管理能力。这些举措和共享材料可以作为未来计划的原型，这些计划在高触摸，包容和令人鼓舞的环境中培养本科生和研究生的相互增长。
Universities have been expanding undergraduate data science programs. Involving graduate students in these new opportunities can foster their growth as data science educators. We describe two programs that employ a near-peer mentoring structure, in which graduate students mentor undergraduates, to (1) strengthen their teaching and mentoring skills and (2) provide research and learning experiences for undergraduates from diverse backgrounds. In the Data Science for Social Good program, undergraduate participants work in teams to tackle a data science project with social impact. Graduate mentors guide project work and provide just-in-time teaching and feedback. The Stanford Mentoring in Data Science course offers training in effective and inclusive mentorship strategies. In an experiential learning framework, enrolled graduate students are paired with undergraduate students from non-R1 schools, whom they mentor through weekly one-on-one remote meetings. In end-of-program surveys, mentors reported growth through both programs. Drawing from these experiences, we developed a self-paced mentor training guide, which engages teaching, mentoring and project management abilities. These initiatives and the shared materials can serve as prototypes of future programs that cultivate mutual growth of both undergraduate and graduate students in a high-touch, inclusive, and encouraging environment.
