• The Journal of Korean Association of Computer Education
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• v.21 no.3
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• pp.51-58
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• 2018

In order to analyze the prediction of the computer entertainment behavior, this study investigated the variables' interdependencies and their causal relations to the computer entertainment behavior using Bayesian inference with the Korean Children and Youth Panel Survey data. For the study, Markov blanket was extracted through General Bayesian Network and the degree of influences was investigated by changing the variables' probabilities. Results showed that the computer entertainment behavior was significantly changed depending on adjusting the values of the related variables; school learning act, smoking, taunting, fandom, and school rule. The results suggested that the Bayesian inference could be used in educational filed for predicting and adjusting the adolescents' computer entertainment behavior.

• The Journal of Korean Association of Computer Education
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• v.16 no.2
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• pp.39-48
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• 2013

This study investigates the predictive model of the adolescents' computer entertainment behavior using neural network with the KYPS data (3449 in the junior high school; 1725 boys and 1724 girls). This study compares the results of neural network(model 1) to the logistic regression model and neural network(model 2) with the exact same variables used in logistic regression. The results reveal that the prediction of neural network model 1 is the highest among three models and with gender, computer use time, family income, the number of close friends, the number of misdeed friends, individual study time, self-control, private education time, leisure time, self-belief, stress, adaptation to school, and study related worries, the neural network model 1 predicts the computer entertainment behavior more efficiently. These results suggest that the neural network could be used for diagnosing and adjusting the adolescents' computer entertainment behavior.

• The Journal of Korean Association of Computer Education
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• v.20 no.2
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• pp.47-55
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• 2017

The purpose of this study is to investigate the predicting factors on the increase in computer entertainment behavior with the sample from KYPS data. The results of the Decision Tree model revealed that: (1) Neighbor supervision, self-belief, parent attachment, life satisfaction, and peer attachment were significant for the increase in computer entertainment behavior. (2) Neighbor supervision, class participation and leisure satisfaction were significant for male students' increase in computer entertainment behavior. (3) Optimistic disposition, teacher attachment, and peer attachment were significant for female students' increase in computer entertainment behavior. These results suggest that meaningful factors and their divers interactions should be considered in methods and programs for regulating and preventing the increase in computer entertainment behavior.