This technical report describes a dataset which contains diachronic data about the everyday life of one hundred fifty-eight university students over a period of four weeks, and also additional synchronic data about profile, e.g., demographics, routines, personality. The diachronic data are collected from thirty-four sensors, both hardware and software, associated to around 100+ thousand self-reported annotations. The dataset has been collected based on an ontological representation of the situational context and following various reference standards, e.g., HETUS and the Big Five. The data collection is motivated by the rise of so-called people-centric sensing paradigm, wherein sensors embedded in mobile phones and other wireless devices are used to collect large quantities of continuous data pertaining to the behavior of individuals and social networks. These datasets offer unique opportunities to investigate the diversity and daily routines of university students in a multi-layered perspective.
A survey on students' daily routines and academic performance at the University of Trento / Giunchiglia, Fausto; Busso, Matteo; Zeni, Mattia; Bison, Ivano. - ELETTRONICO. - (2022), pp. 1-12.
A survey on students' daily routines and academic performance at the University of Trento
Fausto Giunchiglia;Matteo Busso;Ivano Bison
2022-01-01
Abstract
This technical report describes a dataset which contains diachronic data about the everyday life of one hundred fifty-eight university students over a period of four weeks, and also additional synchronic data about profile, e.g., demographics, routines, personality. The diachronic data are collected from thirty-four sensors, both hardware and software, associated to around 100+ thousand self-reported annotations. The dataset has been collected based on an ontological representation of the situational context and following various reference standards, e.g., HETUS and the Big Five. The data collection is motivated by the rise of so-called people-centric sensing paradigm, wherein sensors embedded in mobile phones and other wireless devices are used to collect large quantities of continuous data pertaining to the behavior of individuals and social networks. These datasets offer unique opportunities to investigate the diversity and daily routines of university students in a multi-layered perspective.File | Dimensione | Formato | |
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