Modeling Cyberloafing In Out-Of-Class Learning Activities: The Role of Screen Time, Intrinsic Motivation, Self-regulation and Multitasking

This study examines university students' cyberloafing behaviors during out-ofclass learning activities and focuses on the effects of screen time, multitasking, intrinsic motivation, and self-regulation factors. Cyberloafing is defined as using smartphones or the Internet for non-academic purposes during learning, and it is stated that this behavior can negatively affect productivity and increase exposure to online risks. On the other hand, some studies suggest that cyberloafing can reduce stress and increase creativity. Although there have been studies on cyberloafing in classroom learning activities in the literature, it has been observed that there is a limited number of studies on its effects in out-of-class environments. Within the scope of the study, data were collected from 378 university students through a questionnaire and the data obtained were analyzed using structural equation modeling (SEM). In the study, the effects of multitasking, screen time, intrinsic motivation and self-regulation, cyberloafing and academic achievement variables were examined. In the study, multitasking tendencies were found to significantly affect screen time. In addition, screen time was found to have a significant positive effect on cyberloafing; as screen time increases, the level of cyberloafing also increases. Academic intrinsic motivation was found to increase cyberloafing, while self-regulation had no significant effect on cyberloafing. However, it was concluded that cyberloafing did not significantly affect academic achievement. As a result, multitasking and screen time stand out as factors that increase cyberloafing. Cyberloafing in the context of out-of-class learning has a complex nature. Although multitasking and screen time are significant predictors, they do not directly affect academic achievement. This study included multitasking, screen time, cyberloafing and academic motivation and academic achievement variables in the model. It is suggested that variables such as individual characteristics, digital literacy, artificial intelligence literacy, etc. can be included in the model. In addition, educator policy makers and students can use the variables found to have an impact in this study in their planning to balance cyberloafing in out-of-class learning activities.