As many Korean universities have recommended the implementation of mobile learning (m-learning) for various reasons, the number of such tertiary learning opportunities has steadily grown. However, little research has investigated the factors affecting university students' adoption and use of m-learning. A sample of 288 Konkuk university students participated in the research.The process by which students adopt m-learning was explained using structural equation modeling technique and the Linear Structural Relationship (LISREL) program.The general structural model based on the technology acceptance model included m-learning self-efficacy, relevance for students' major (MR), system accessibility, subjective norm (SN), perceived usefulness, perceived ease of use, attitude (AT), and behavioral intention to use m-learning.The study results confirmed the acceptability of the model to explain students' acceptance of m-learning. M-learning AT was the most important construct in explaining the causal process in the model, followed by students' MR and SN.

Educators try to use social network services (SNSs) in their teaching because they are regarded as beneficial to student learning. However, little research to date has empirically investigated whether the use of an SNS increases university student learning outcomes. A total of 730 university students in the capital area of South Korea participated in the present study. Based on social learning theory, we developed a general structural model that included SNS participation (SP) as an exogenous variable and a range of endogenous variables: cognitive domain learning outcomes, affective domain learning outcomes, social domain learning outcomes, social acceptance (SA) and attitude towards university life (AT). The study results confirmed that the model adequately explained causal relationships between student learning outcomes and SA, AT and SP. SA was identified as a key variable because of its direct effect on learning outcomes. However, SP was considered more important because of its large indirect effects on all learning outcomes through both SA and AT.

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For the purpose of enhancing the use of social service in classrooms, this research focuses on the relationships between presence and learning flow, key words in the analysis of college classes using Facebook. The results of this study are as follow. First, social presence(=.33, p=.000), emotional presence(= .29, p= .000), cognitive presence(= .20, p= .010) were found to be significant according to cognitive flow experience the result of analysis of multiple regression. all regression coefficients were positive. Second, emotional presence(=.42, p=.000) and social presence(=.27, p=.000), cognitive, presence(= .17, p=.015) were found to be significant according to emotional flow experience the result of analysis of multiple regression. all regression coefficients were positive. Third, social presence(=.37, p=.000) of the three variables were found to be significant according to behavioral flow experience the result of analysis of multiple regression.

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