Development of a Multiple Linear Regression Model for Predicting the Impact of Online Gaming on Students’ Learning Behavior
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Abstract
This research aimed to develop a multiple linear regression model for predicting the impact of online gaming on students' learning behavior. The sample consisted of 400 science students from the University of Ubon Ratchathani, divided into a training set of 380 and a testing set of 20. Data were collected using a 5-level Likert scale questionnaire with an average IOC of 0.90 and Cronbach's alpha of 0.91. The independent variables were X1 (gaming frequency per day) and X2 (gaming time per day), with Y (level of impact on learning behavior) as the dependent variable. The resulting equation was Ŷ = 1.000 + 0.303(X1) + 0.311(X2), with R² = 0.579, explaining 57.91% of the variance in Y at a significance level of 0.001. Additionally, a Cylindrical Behavior Model was developed to transform results into cylindrical coordinates (r, θ, z) for three-dimensional behavioral profiling, and implemented as a prototype tool for academic advising.
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