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

被引:16
|
作者
Wang, Weichen [1 ]
Nepal, Subigya [1 ]
Huckins, Jeremy F. [2 ]
Hernandez, Lessley [1 ]
Vojdanovski, Vlado [1 ]
Mack, Dante [2 ]
Plomp, Jane [2 ]
Pillai, Arvind [1 ]
Obuchi, Mikio [1 ]
Dasilva, Alex [2 ]
Murphy, Eilis [2 ]
Hedlund, Elin [2 ]
Rogers, Courtney [2 ]
Meyer, Meghan [2 ]
Campbell, Andrew [1 ]
机构
[1] Dartmouth Coll, Comp Sci, Hanover, NH 03755 USA
[2] Dartmouth Coll, Psychol & Brain Sci, Hanover, NH 03755 USA
关键词
mobile sensing; mental health; first-generation students; first year; ACADEMIC-PERFORMANCE; SOCIOECONOMIC-STATUS; SOCIAL SUPPORT; DEPRESSION; EXPERIENCES; TRANSITION; ATTRITION; SEVERITY; BEHAVIOR; SUCCESS;
D O I
10.1145/3543194
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页数:32
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