First-Gen Lens: Assessing Mental Health of First-Generation Students across Their First Year at College Using Mobile Sensing.

first year first-generation students mental health mobile sensing

Journal

Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies
ISSN: 2474-9567
Titre abrégé: Proc ACM Interact Mob Wearable Ubiquitous Technol
Pays: United States
ID NLM: 101719413

Informations de publication

Date de publication:
Jul 2022
Historique:
entrez: 23 12 2022
pubmed: 24 12 2022
medline: 24 12 2022
Statut: ppublish

Résumé

The transition from high school to college is a taxing time for young adults. New students arriving on campus navigate a myriad of challenges centered around adapting to new living situations, financial needs, academic pressures and social demands. First-year students need to gain new skills and strategies to cope with these new demands in order to make good decisions, ease their transition to independent living and ultimately succeed. In general, first-generation students are less prepared when they enter college in comparison to non-first-generation students. This presents additional challenges for first-generation students to overcome and be successful during their college years. We study first-year students through the lens of mobile phone sensing across their first year at college, including all academic terms and breaks. We collect longitudinal mobile sensing data for N=180 first-year college students, where 27 of the students are first-generation, representing 15% of the study cohort and representative of the number of first-generation students admitted each year at the study institution, Dartmouth College. We discuss risk factors, behavioral patterns and mental health of first-generation and non-first-generation students. We propose a deep learning model that accurately predicts the mental health of first-generation students by taking into account important distinguishing behavioral factors of first-generation students. Our study, which uses the StudentLife app, offers data-informed insights that could be used to identify struggling students and provide new forms of phone-based interventions with the goal of keeping students on track.

Identifiants

pubmed: 36561350
doi: 10.1145/3543194
pmc: PMC9770714
mid: NIHMS1854607
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : NIMH NIH HHS
ID : R01 MH059282
Pays : United States

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