TY - GEN
T1 - Using Unsupervised Machine Learning to Model Taiwanese High-School Students' Digital Distraction Profiles Concerning Internet Gaming Disorder
AU - Ho, Yu Lin
AU - Chou, Chien
AU - Liao, Chen Hsuan
AU - Wu, Jiun Yu
N1 - Publisher Copyright:
© 30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings.
PY - 2022/11/28
Y1 - 2022/11/28
N2 - 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.
AB - 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.
KW - Digital Distraction
KW - Internet Gaming Disorder
KW - Learner
KW - Machine learning in Education
KW - Self-Regulation Strategies
KW - Student Modeling
UR - http://www.scopus.com/inward/record.url?scp=85151053561&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85151053561
T3 - 30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings
SP - 32
EP - 37
BT - 30th International Conference on Computers in Education Conference, ICCE 2022 - Proceedings
A2 - Iyer, Sridhar
A2 - Shih, Ju-Ling
A2 - Shih, Ju-Ling
A2 - Chen, Weiqin
A2 - Chen, Weiqin
A2 - Khambari, Mas Nida MD
A2 - Denden, Mouna
A2 - Majumbar, Rwitajit
A2 - Medina, Liliana Cuesta
A2 - Mishra, Shitanshu
A2 - Murthy, Sahana
A2 - Panjaburee, Patcharin
A2 - Sun, Daner
PB - Asia-Pacific Society for Computers in Education
T2 - 30th International Conference on Computers in Education Conference, ICCE 2022
Y2 - 28 November 2022 through 2 December 2022
ER -