Predicting the Quality of User Experiences to Improve Productivity and Wellness
Abstract
College students often struggle to balance their work with personal wellness. In part, this occurs because students work when they are unable to focus. We hypothesize that we can adapt the Experience Sampling Method (ESM) to build a model of users’ efficacy and predict when they will be most likely to experience flow, a state of motivation and immersion. We also hypothesize that we can present this information effectively to users, allowing them to understand when they are most likely to achieve flow. In order to test these hypotheses, we introduce the Productivity and Wellness Pal (PaWPal), a smartphone-based application that seeks to make users aware of their efficacy at various tasks as well as which courses of action are likely to lead to immersive experiences.
Cite
Text
Donti et al. "Predicting the Quality of User Experiences to Improve Productivity and Wellness." AAAI Conference on Artificial Intelligence, 2015. doi:10.1609/AAAI.V29I1.9740Markdown
[Donti et al. "Predicting the Quality of User Experiences to Improve Productivity and Wellness." AAAI Conference on Artificial Intelligence, 2015.](https://mlanthology.org/aaai/2015/donti2015aaai-predicting/) doi:10.1609/AAAI.V29I1.9740BibTeX
@inproceedings{donti2015aaai-predicting,
title = {{Predicting the Quality of User Experiences to Improve Productivity and Wellness}},
author = {Donti, Priya Lekha and Rosenbloom, Jacob and Gruver, Alex and Jr., James C. Boerkoel},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2015},
pages = {4154-4155},
doi = {10.1609/AAAI.V29I1.9740},
url = {https://mlanthology.org/aaai/2015/donti2015aaai-predicting/}
}