Using Unsupervised Machine Learning to Model Taiwanese High-School Students’ Digital Distraction Profiles Concerning Internet Gaming Disorder

Abstract

Digital distraction is cognitive attention wandering or being directed to digital sources other than the main learning task. Adolescents with digital distractions may also be addicted to Internet gaming or even suffer Internet gaming disorder (IGD), severely harming their physical and mental development and learning performance. Digital distraction is related to low self-esteem, which is one of the antecedents of IGD. Therefore, this study investigates the association between digital distraction and IGD. We collected responses from 793 Taiwanese senior high students. Results showed that students utilize digital devices for entertainment when learning. Two-stage clustering classified students into four groups concerning their digital distraction constructs: perceived attention problems (PAP) and self-regulation strategies(SRS). The IGD-suspected participants were in the groups with strong PAP profile. We found that digital distraction would be associated with IGD. To mitigate IGD, we suggest early digital distraction screening and provide self-regulation strategies for high schoolers to mitigate their attention and IGD issues.