The purpose of the present study was to identify latent classes resting on early adolescents' change trajectory patterns in using computers and the Internet for learning and to test the effects of gender, self-control, self-esteem, and game use in South Korea. Latent growth mixture modeling (LGMM) was used to identify subpopulations in the Korea Youth Panel Survey (KYPS). Initial analyses revealed four latent classes: High Use Class, Increasing Class, Decreasing Class, and Low Use Class. Adding gender, self-control, self-esteem, and game use, we tested the effects of the independent variables on the latent classes using multinomial logistic analysis. Results from the second analyses indicated that gender, self-control, self-esteem, and game use were significant determinants of the latent classes. Findings suggest the need to consider heterogeneity in studies of early adolescents' use of computers and the Internet for learning in order to better target involvement programs. (Contains 2 figures and 5 tables.)