Predicting students' happiness from physiology, phone, mobility, and behavioral data
Name
Picard_Predicting students.pdf
Size
435.34 KB
Format
Adobe PDF
Checksum (MD5)
a0bf879a7c7fce2cad59111105afc225
Author(s)
Jaques, Natasha Mary
Taylor, Sara Ann
Azaria, Asaph Mordehai Assaf
Ghandeharioun, Asma
Sano, Akane
Picard, Rosalind W.
Date Issued
December 2015
Journal
2015 International Conference on Affective Computing and Intelligent Interaction (ACII)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Jaques, Natasha et al. “Predicting Students’ Happiness from Physiology, Phone, Mobility, and Behavioral Data.” 2015 International Conference on Affective Computing and Intelligent Interaction (ACII) 222–228. © 2017 IEEE
Version
Author's final manuscript
Abstract
In order to model students' happiness, we apply machine learning methods to data collected from undergrad students monitored over the course of one month each. The data collected include physiological signals, location, smartphone logs, and survey responses to behavioral questions. Each day, participants reported their wellbeing on measures including stress, health, and happiness. Because of the relationship between happiness and depression, modeling happiness may help us to detect individuals who are at risk of depression and guide interventions to help them. We are also interested in how behavioral factors (such as sleep and social activity) affect happiness positively and negatively. A variety of machine learning and feature selection techniques are compared, including Gaussian Mixture Models and ensemble classification. We achieve 70% classification accuracy of self-reported happiness on held-out test data.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ACII.2015.7344575
