2022 Research in the Capitol
Location
Iowa State House, Rotunda
Presentation Type
Open Access Poster Presentation
Keywords
Social status; Students--Social conditions; Mobile apps;
Abstract
Loneliness, isolation, and anti-social behaviors have increased in the past few years, whether that be due to social media, people paying more attention to their devices, or due to the COVID-19 pandemic. These behaviors are proven to decrease a student’s academic performance, causing their grades to decline, and disabling their motivation to learn. We aim to gain insight on this issue via the application of smartphone technology and machine learning, enabling those that use our app to understand if their being social or anti-social. We use a variety of sensors, location devices, and speaker recognition algorithms to identify behaviors that help us let the user know when they’re being negatively affected by their social behavior. Our end goal is to be able to tell students and users a “social score” after an interval of time, helping them identify and fix when they’re being overly isolated or lonely.
Start Date
21-2-2022 11:30 AM
End Date
21-2-2022 1:30 PM
Event Host
University Honors Programs, Iowa Regent Universities
Faculty Advisor
Dheryta Jaisinghani
Department
Department of Computer Science
Copyright
©2022 Aaron Walker
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
File Format
application/pdf
Recommended Citation
Walker, Aaron, "SocioApp: Detecting Your Sociability Status with Your Smartphone" (2022). Research in the Capitol. 4.
https://scholarworks.uni.edu/rcapitol/2022/all/4
SocioApp: Detecting Your Sociability Status with Your Smartphone
Iowa State House, Rotunda
Loneliness, isolation, and anti-social behaviors have increased in the past few years, whether that be due to social media, people paying more attention to their devices, or due to the COVID-19 pandemic. These behaviors are proven to decrease a student’s academic performance, causing their grades to decline, and disabling their motivation to learn. We aim to gain insight on this issue via the application of smartphone technology and machine learning, enabling those that use our app to understand if their being social or anti-social. We use a variety of sensors, location devices, and speaker recognition algorithms to identify behaviors that help us let the user know when they’re being negatively affected by their social behavior. Our end goal is to be able to tell students and users a “social score” after an interval of time, helping them identify and fix when they’re being overly isolated or lonely.
