First-Gen Lens

Author:

Wang Weichen1,Nepal Subigya1,Huckins Jeremy F.2,Hernandez Lessley1,Vojdanovski Vlado1,Mack Dante2,Plomp Jane2,Pillai Arvind1,Obuchi Mikio1,daSilva Alex2,Murphy Eilis2,Hedlund Elin2,Rogers Courtney2,Meyer Meghan2,Campbell Andrew1

Affiliation:

1. Dartmouth College, Computer Science, Hanover, NH, USA

2. Dartmouth College, Psychological and Brain Sciences, Hanover, NH, USA

Abstract

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.

Funder

NIMH

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture,Human-Computer Interaction

Reference95 articles.

1. Automatic detection of social rhythms in bipolar disorder

2. Daniel A Adler , Dror Ben-Zeev , Vincent WS Tseng , John M Kane, Rachel Brian, Andrew T Campbell, Marta Hauser, Emily A Scherer, and Tanzeem Choudhury. 2020 . Predicting early warning signs of psychotic relapse from passive sensing data: an approach using encoder-decoder neural networks. JMIR mHealth and uHealth 8, 8 (2020), e19962. Daniel A Adler, Dror Ben-Zeev, Vincent WS Tseng, John M Kane, Rachel Brian, Andrew T Campbell, Marta Hauser, Emily A Scherer, and Tanzeem Choudhury. 2020. Predicting early warning signs of psychotic relapse from passive sensing data: an approach using encoder-decoder neural networks. JMIR mHealth and uHealth 8, 8 (2020), e19962.

3. Apple 2021. Extending Your App's Background Execution Time. https://developer.apple.com/documentation/uikit/app_and_environment/scenes/preparing_your_ui_to_run_in_the_background/extending_your_app_s_background_execution_time. Apple 2021. Extending Your App's Background Execution Time. https://developer.apple.com/documentation/uikit/app_and_environment/scenes/preparing_your_ui_to_run_in_the_background/extending_your_app_s_background_execution_time.

4. AWARE framework 2013. Aware: Open-source Context Instrumentation Framework For Everyone. http://www.awareframework.com/. AWARE framework 2013. Aware: Open-source Context Instrumentation Framework For Everyone. http://www.awareframework.com/.

5. Adaptive linear step-up procedures that control the false discovery rate

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