Abstract
Positive feelings are essential for students’ well-being and are associated with their academic performance and long-term development. While prior studies have revealed relationships between certain events (e.g., activities and social interactions) and student feelings, little attention was paid to the influence of event durations. In order to address this gap, the present study investigates how time spent on daily activities (e.g., studying) and interactions with social companions (e.g., family/friends) predict adolescent students’ positive feelings. Moreover, the potential moderating roles of personal factors (e.g., health consciousness) were considered. We collected longitudinal data associated with the physical, social, emotional, and digital well-being of 36 middle school students in Hong Kong consecutively for three weeks, using a day reconstruction method. In total, 279 reconstructed days with 2433 events have been recorded. Hierarchical linear modelling was then employed to analyse the nested relationships between events, positive feelings, and personal factors. Results indicated several significant associations between time allocated to daily activities/social interactions and duration of positive feelings. Furthermore, we found that personal factors such as mental health and academic engagement were not only significantly associated with duration of positive feelings but also moderated the relationships between daily activities/social interactions and positive feelings. Copyright © 2023 National Institute of Education, Singapore.
Original language | English |
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Journal | Asia Pacific Journal of Education |
Early online date | Jun 2023 |
DOIs | |
Publication status | E-pub ahead of print - Jun 2023 |
Citation
Ba, S., Hu, X., & Law, N. (2023). Daily activities and social interactions predict students’ positive feelings. Asia Pacific Journal of Education. Advance online publication. https://doi.org/10.1080/02188791.2023.2219414Keywords
- Adolescent
- Well-being
- Positive feeling
- Day reconstruction method
- Hierarchical linear modelling