Adaptation of student behavioural routines during Covid-19: a multimodal approach - Nicolò Alessandro Girardini; Simone Centellegher; Andrea Passerini; Ivano Bison; Fausto Giunchiglia; Bruno Lepri (EPJ Data Science, 2023)
Abstract One population group that had to significantly adapt and change their behaviour during the COVID-19 pandemic is students. While previous studies have extensively investigated the impact of the pandemic on their psychological well-being and academic performance, limited attention has been given to their activity routines. In this work, we analyze students’ behavioural changes by examining qualitative and quantitative differences in their daily routines between two distinct periods (2018 and 2020). Using an Experience Sampling Method (ESM) that captures multimodal self-reported data on students’ activity, locations and sociality, we apply Non-Negative Matrix Factorization (NMF) to extract meaningful behavioural components, and quantify the variations in behaviour between students in 2018 and 2020. Surprisingly, despite the presence of COVID-19 restrictions, we find minimal changes in the activities performed by students, and the diversity of activities also remains largely unaffected. Leveraging the richness of the data at our disposal, we discover that activities adaptation to the pandemic primarily occurred in the location and sociality dimensions.