Abstract
Although Bayesian Networks have fascinated researchers in many fields, they have been hardly used in educational domain due to their complexity. In this paper, we adopt Bayesian Networks to discover which variables are in charge of youth stress and how much the variables influence. In order to learn a Bayesian Network and to choose the relevant variables, Korean Children Panel Survey (KCPS) data are used and Markov Blanket is adopted. From the analysis, “aggressiveness”, “depression”, “worry about studying”, “parent’s expectation of study”, and “interest in school study” are extracted as the relevant variables to youth stress, therefore caregivers should pay attention to these variables to relieve youth stress. This paper shows Bayesian Networks are quite effective in finding the causal variables and their effects in educational domain.
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Jung, E. (2019). Analysis on Variables Affecting Youth Stress with Bayesian Networks. In: Hung, J., Yen, N., Hui, L. (eds) Frontier Computing. FC 2018. Lecture Notes in Electrical Engineering, vol 542. Springer, Singapore. https://doi.org/10.1007/978-981-13-3648-5_53
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DOI: https://doi.org/10.1007/978-981-13-3648-5_53
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