Near-Peer Mentoring in Data Science: A Plot for Mutual Growth
Chiara Sabatti, Qian Zhao

TL;DR
This paper presents two near-peer mentoring programs in data science that enhance teaching skills for graduate students and provide research opportunities for undergraduates from diverse backgrounds, fostering mutual growth.
Contribution
It introduces two novel mentoring programs that integrate experiential learning and inclusive strategies to support undergraduate and graduate student development in data science.
Findings
Mentors reported growth in teaching and mentoring skills.
Undergraduates gained research and learning experiences.
Programs serve as prototypes for inclusive data science education.
Abstract
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…
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Taxonomy
TopicsInnovative Teaching and Learning Methods
