Academic Journal
Near-Peer Mentoring in Data Science: A Plot for Mutual Growth.
| Τίτλος: | Near-Peer Mentoring in Data Science: A Plot for Mutual Growth. |
|---|---|
| Συγγραφείς: | Sabatti, Chiara, Zhao, Qian |
| Πηγή: | American Statistician; Nov2025, Vol. 79 Issue 4, p529-537, 9p |
| Θεματικοί όροι: | Data science, Graduate students, Experiential learning, Undergraduates, Holistic education, Mentoring, Effective teaching |
| Περίληψη: | 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. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Near-Peer Mentoring in Data Science: A Plot for Mutual Growth. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sabatti%2C+Chiara%22">Sabatti, Chiara</searchLink><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Qian%22">Zhao, Qian</searchLink> – Name: TitleSource Label: Source Group: Src Data: American Statistician; Nov2025, Vol. 79 Issue 4, p529-537, 9p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Data+science%22">Data science</searchLink><br /><searchLink fieldCode="DE" term="%22Graduate+students%22">Graduate students</searchLink><br /><searchLink fieldCode="DE" term="%22Experiential+learning%22">Experiential learning</searchLink><br /><searchLink fieldCode="DE" term="%22Undergraduates%22">Undergraduates</searchLink><br /><searchLink fieldCode="DE" term="%22Holistic+education%22">Holistic education</searchLink><br /><searchLink fieldCode="DE" term="%22Mentoring%22">Mentoring</searchLink><br /><searchLink fieldCode="DE" term="%22Effective+teaching%22">Effective teaching</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of American Statistician is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00031305.2025.2550314 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 529 Subjects: – SubjectFull: Data science Type: general – SubjectFull: Graduate students Type: general – SubjectFull: Experiential learning Type: general – SubjectFull: Undergraduates Type: general – SubjectFull: Holistic education Type: general – SubjectFull: Mentoring Type: general – SubjectFull: Effective teaching Type: general Titles: – TitleFull: Near-Peer Mentoring in Data Science: A Plot for Mutual Growth. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sabatti, Chiara – PersonEntity: Name: NameFull: Zhao, Qian IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 00031305 Numbering: – Type: volume Value: 79 – Type: issue Value: 4 Titles: – TitleFull: American Statistician Type: main |
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