Predicting students' happiness from physiology, phone, mobility, and behavioral data. Jaques, N., Taylor, S., Azaria, A., Ghandeharioun, A., Sano, A., & Picard, R. In 2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015, volume 2015, pages 222-228, 12, 2015. Institute of Electrical and Electronics Engineers Inc.. ![Predicting students' happiness from physiology, phone, mobility, and behavioral data [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website abstract bibtex 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.
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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.},
bibtype = {inproceedings},
author = {Jaques, Natasha and Taylor, Sara and Azaria, Asaph and Ghandeharioun, Asma and Sano, Akane and Picard, Rosalind},
booktitle = {2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015}
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