PeerConnect: Co-Designing a Peer-Mentoring Support System with Computing Transfer Students

No Thumbnail Available

Links to Files

https://dl.acm.org/doi/10.1145/3613905.3650936

Permanent Link

https://doi.org/10.1145/3613905.3650936
 http://hdl.handle.net/11603/34636

Collections

UMBC Faculty Collection
UMBC Information Systems Department
UMBC Student Collection

Author/Creator

Author/Creator ORCID

https://orcid.org/0009-0001-5168-9616
 https://orcid.org/0009-0004-7923-7170
 https://orcid.org/0009-0001-6422-3268
 https://orcid.org/0000-0002-7185-8405
 https://orcid.org/0000-0003-1007-2553

Date

2024-05-11

Type of Work

conference papers and proceedings
Text

Department

Program

Citation of Original Publication

Anthraper, Nisha, Prachee Javiya, Sai Iluru, Lujie Karen Chen, and Andrea Kleinsmith. "PeerConnect: Co-Designing a Peer-Mentoring Support System with Computing Transfer Students." In Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems, 1-7 CHI EA '24. New York, NY, USA: Association for Computing Machinery, 2024. https://doi.org/10.1145/3613905.3650936.

Rights

Subjects

co-design
learning analytics
peer mentoring
sense of belonging

Abstract

In the US, nearly half of the STEM undergraduates begin their academic careers at community colleges. Transferring to four-year institutions can be challenging. Evidence suggests that mentoring can help by increasing a sense of belonging and retention. We engaged mentors and mentees from a pilot mentoring program for new transfer students in computing majors at a minority-serving institution in the Northeastern US in a co-design workshop to understand their needs and requirements for a peer-mentoring system, PeerConnect. PeerConnect aims to foster transfer students's academic and social engagement, increase self-efficacy and belonging, and develop students' self-regulated learning skills. Preliminary results show that students want features that push the system beyond merely measuring engagement to actively promoting it. This study contributes to HCI and CSCW work in designing support systems for mentoring and peer support programs in educational settings and to the emerging literature on student-centered learning analytics systems.