Investigating School Absenteeism and Refusal among Australian Children and Adolescents using Apriori Association Rule Mining
Article
Article Title | Investigating School Absenteeism and Refusal among Australian Children and Adolescents using Apriori Association Rule Mining |
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ERA Journal ID | 201487 |
Article Category | Article |
Authors | Haque, Umme Marzia, Kabir, Enamul and Khanam, Rasheda |
Journal Title | Scientific Reports |
Journal Citation | 14 |
Article Number | 1907 |
Number of Pages | 11 |
Year | 2024 |
Publisher | Nature Publishing Group |
Place of Publication | United Kingdom |
ISSN | 2045-2322 |
Digital Object Identifier (DOI) | https://doi.org/10.1038/s41598-024-51230-4 |
Web Address (URL) | https://www.nature.com/articles/s41598-024-51230-4 |
Abstract | Identifying and determining the multitude of reasons behind school absences of students is often challenging. This study aims to uncover the hidden reasons for school absence in children and adolescents. The analysis is conducted on a national survey that includes 2967 Australian children and adolescents aged 11–17. The Apriori association rule generator of machine learning techniques and binary logistic regression are used to identify the significant predictors of school absences. Out of 2484, 83.7% (n = 2079) aged (11–17) years children and adolescents have missed school for various reasons, 42.28% (n = 879) are (11–15) years old, 24.52% (n = 609) and 16.9% (n = 420) are 16- and 17-years old adolescents respectively. A considerable proportion of adolescents, specifically 16.4% (n = 407) and 23.4% (n = 486) of 16 and 17 years old, respectively, have selected ‘refused to say’ as their reason for not attending school. It also highlights the negative outcomes associated with undisclosed reasons for school absence, such as bullying, excessive internet/gaming, reduced family involvement, suicide attempts, and existential hopelessness. The findings of the national survey underscore the importance of addressing these undisclosed reasons for school absence to improve the overall well-being and educational outcomes of children and adolescents. |
Related Output | |
Is part of | Enhancing early detection of psychological disorders in children and adolescents using ML: a comprehensive mental health assessment |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460502. Data mining and knowledge discovery |
Public Notes | This article is part of a UniSQ Thesis by publication. See Related Output. |
Byline Affiliations | School of Mathematics, Physics and Computing |
School of Business |
https://research.usq.edu.au/item/z5839/investigating-school-absenteeism-and-refusal-among-australian-children-and-adolescents-using-apriori-association-rule-mining
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