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This Innovative Practice paper describes the Local Research Experiences for Undergraduates (LREU) program that was established by the Computing Alliance of Hispanic-Serving Institutions (CAHSI) at Hispanic-serving institutions (HSIs) in 2021 to increase the number of students, particularly students from underrepresented populations, who enter graduate programs in computer science. Since its first offering in Spring 2022, the LREU program has involved 182 faculty and 253 students. The LREU program funds undergraduate research experiences at the students’ home institutions with an emphasis on first-generation students and those with financial needs. The motivation for the program is to address the low number of domestic students, particularly Hispanics and other minoritized populations, who seek and complete graduate degrees. Research shows that participation in research activities predicts college outcomes such as GPA, retention, and persistence. Even though these studies inform us of the importance of REU programs, many programmatic efforts are summer experiences and, while students may receive support, faculty mentors rarely receive coaching or professional development efforts. What distinguishes the LREU program is the focus on the deliberative development of students’ professional and research skills; faculty coaching on the Affinity Research Group model; and the learning community established to share experiences and practices and to learn from each other. Students, who are matched with faculty mentors based on their areas of interest, work with their mentor to co-create a research plan. Students keep a research journal in which they record what they have learned and identify areas for their growth and development as researchers. The LREU provides an opportunity for the LREU participants to cultivate a growth mindset through deliberate practice and reflection from personal, professional, social, and academic perspectives. The paper discusses the multi-institutional perspectives that help CAHSI understand the types of challenges faced in undergraduate research programs, how faculty mentors communicate and make decisions, and how mentors resolve challenges, allowing the research community to better understand students’ and faculty experiences. In addition, the paper reports on research and evaluation results that documented mentors’ growth in their knowledge of effective research mentoring practices and students’ learning gains in research and other skills. The paper also describes the impact of the learning community, e.g., how it supports developing strategies for interaction with and mentoring students from underrepresented populations.
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This content will become publicly available on October 2, 2026
Near-Peer Mentoring in Data Science: A Plot for Mutual Growth
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 (a) strengthen their teaching and mentoring skills and (b) 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.
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- Award ID(s):
- 2210392
- PAR ID:
- 10647339
- Publisher / Repository:
- Taylor & Francis
- Date Published:
- Journal Name:
- The American Statistician
- Volume:
- 79
- Issue:
- 4
- ISSN:
- 0003-1305
- Page Range / eLocation ID:
- 529 to 537
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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