Abstract
Aggression formed in children lasts for their lifetime and it often produces serious antisocial problems. Therefore, a lot of studies have been conducted for this subject in the educational domain, but the studies adopting data mining are relatively few yet. In this paper, the authors adopt Bayesian Networks to find which variables are related to aggression and to evaluate how the variables affect it. Markov Blanket and IMDB method are used to learn a Bayesian Network and to find the relevant variables. In the results, “social withdrawal”, “depression”, “mobile phone dependency”, “grade of Korean”, “attention” and “school activity” are extracted as the relevant variables to aggression. Also, this research investigates which variables are most influencing to aggression by changing the probabilities of variables in the learned Bayesian Network.
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Acknowledgements
This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2018S1A5B5A07072578)
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Lee, H., Jung, E. (2021). Exploration of a Prediction Model of Aggression in Children Using Bayesian Networks. In: Kim, H., Kim, K.J. (eds) IT Convergence and Security. Lecture Notes in Electrical Engineering, vol 712. Springer, Singapore. https://doi.org/10.1007/978-981-15-9354-3_4
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DOI: https://doi.org/10.1007/978-981-15-9354-3_4
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