• Journal of Industrial Convergence
• /
• v.19 no.3
• /
• pp.27-34
• /
• 2021

In the era of the 4th industrial revolution, the age of artificial intelligence, the development of ICT technology is having various effects on the online and offline educational environment. The universal access of online education changes the educational paradigm and converts it to a learner-centered service. At the time when a new educational environment is required to change, interest in flip learning is increasing. Even adult learner's online learning needs is also shown very high. The purpose of this study was to investigate how repetitive learning activities through flip learning for middle-aged online learners of K-Cyber University has a relationship and structural relationship between the effects of learning immersion and learning interest on learning satisfaction. Through this study, there is significance in research to suggest direction for learning satisfaction based on flip learning. For further studies, if a model of analysis of various factors that can be measured is specified and applied, it can be used as a research background that can maximize learning satisfaction based on flip learning.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.9 no.6
• /
• pp.1830-1837
• /
• 2008

The growth of an information and knowledge based society has changed the base of education from institution-based to a learner-based system. This indicates that the educational purpose and individual characters of the learners are the primary factors for the educational success. In the information and knowledge based society, the Cyber University is a representative example of the new educational paradigm with its online communities, multi-media based education and communication among the learners. The sample of study was 1620 students of a leading cyber university in Seoul, Korea. One of the results in this study showed that satisfaction levels of learning and education do not have significant relationship with age or employment. Rather the lowering level of satisfaction after sufficient adaptation period of cyber education was raised as rising problem.

• International journal of advanced smart convergence
• /
• v.4 no.2
• /
• pp.34-37
• /
• 2015

An ideal teaching-learning method, such as the blended learning method, is to motivate interests in education and to allow active class participation of students. Students exposed to this method are hypothesized to be dedicated in learning and their school life. A research was conducted on $11^{th}$ graders in Daejeon city high school specialized in industry; the blended learning method was applied to a course, digital logic circuit and the effects on the students' learning were monitored. The result shows that compared with a common leaning method, the blended learning method is very effective in terms of increasing educational interest, class participation, the level of concentration in class and academic achievement of students. Also, it shows positive feedbacks from the students on the educational videos and the usage of the contents. Conclusively, the blended learning method effectively increases academic achievements through improved educational motivation and active class participation which positively affect the overall satisfaction of participants.

• The Journal of the Korea Contents Association
• /
• v.19 no.12
• /
• pp.129-140
• /
• 2019

This study investigated cyber university students' motivation and demotivation in learning English. Data was collected from a survey of 498 students in a general English course at a cyber university located in Seoul. The survey inquired into participants' English learning motivation and demotivation. To analyze the data, frequency analysis, descriptive statistics, t-test, and ANOVA were used. The findings reveal that among the motivation factors ideal L2 self was the main cause of motivation followed by promotion-based instrumentality. Among the demotivation factors the anxiety factor was found to have the highest mean followed by negative investment value for learning English. The statistical analysis of English learning motivation and demotivation according to the participants' characteristics indicates that, in terms of English learning motivation factors, the male participants' ought-to L2 self was significantly higher than that of the females' and the promotion-based instrumentality of the students who are unemployed was higher than those who are employed. Moreover, the younger the students' age, the higher their competitive motivation and promotion-based instrumentality. In terms of English learning demotivation, the female respondents achieved higher scores in the factors of anxiety, passive learning style, and negative investment value for learning English than their male counterparts. In addition, employed students showed higher demotivation in negative investment value for learning English than those without employment. The findings of this research can be used in developing online English programs for cyber university students who possess diverse learning goals.

• Journal of the Korean Applied Science and Technology
• /
• v.40 no.3
• /
• pp.374-384
• /
• 2023

Universities that previously targeted in 20s have recently diversified their operation methods, founding purposes, and target. With a significant decrease in the school-age population relative to the number of universities, universities are making their best efforts to secure new students and minimize student attrition. In this study, an online survey was conducted to empirically examine the effects of motivation for major selection and sense of belonging on learning persistence intention among students in beauty-related departments at 2-year and 4-year general universities and cyber universities. The collected data from 119 students at general universities and 113 students at cyber universities were analyzed using SPSS 28. The key findings can be summarized as follows: For general universities, motivation for major selection did not have a significant effect on learning persistence intention, but sense of belonging had a significant positive effect. Additionally, an interaction effect was observed, indicating that as the sense of belonging increased, extrinsic motivation significantly increased learning persistence intention. For cyber universities, intrinsic motivation and sense of belonging among motivations for major selection had a significant positive effect on learning persistence intention, while the moderating effect of sense of belonging in the relationship between motivation for major selection and learning persistence intention was not significant. In summary, for general universities, the factor that influenced students' learning persistence intention was a sense of belonging to the university, while for cyber universities, intrinsic motivation played a significant role. These findings are expected to provide meaningful insights and data for universities to develop effective policies for preventing student attrition.

• 전자공학회논문지 IE
• /
• v.48 no.4
• /
• pp.53-60
• /
• 2011

The purpose of this study was to examine the relationship among academic self-efficacy, learning time and environment management, teaching efficacy, flow and satisfaction of cyber university students. For this purpose, the 317 students of W cyber university were participated in the web-survey systems for two weeks at the end of the first semester in 2011. The results of this study through multiple regression analysis indicated that academic self-efficacy, learning time and environment management, and teaching efficacy significantly predicted on flow(${\beta}$=.712, p<.05) and satisfaction(${\beta}$=.531, p<.05). In addition to this, flow was used as a significant mediated variable in the relationships among academic self-efficacy, learning time and environment management, teaching efficacy, and satisfaction. Based on these study results, effective management strategies for improving cyber university students' learning achievement and satisfaction were proposed.

• Journal of Internet Computing and Services
• /
• v.23 no.6
• /
• pp.39-48
• /
• 2022

Cyberattacks around the world continue to increase, and their damage extends beyond government facilities and affects civilians. These issues emphasized the importance of developing a system that can identify and detect cyber anomalies early. As above, in order to effectively identify cyber anomalies, several studies have been conducted to learn BGP (Border Gateway Protocol) data through a machine learning model and identify them as anomalies. However, BGP data is unbalanced data in which abnormal data is less than normal data. This causes the model to have a learning biased result, reducing the reliability of the result. In addition, there is a limit in that security personnel cannot recognize the cyber situation as a typical result of machine learning in an actual cyber situation. Therefore, in this paper, we investigate BGP (Border Gateway Protocol) that keeps network records around the world and solve the problem of unbalanced data by using SMOTE. After that, assuming a cyber range situation, an autoencoder classifies cyber anomalies and visualizes the classified data. By learning the pattern of normal data, the performance of classifying abnormal data with 92.4% accuracy was derived, and the auxiliary index also showed 90% performance, ensuring reliability of the results. In addition, it is expected to be able to effectively defend against cyber attacks because it is possible to effectively recognize the situation by visualizing the congested cyber space.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.7
• /
• pp.380-390
• /
• 2020

Cyber-university has a higher rate of dropout freshmen due to various educational factors, such as social background, economic factors, IT knowledge, and IT utilization ability than students in twenty offline-based university. These students require a different dropout prevention method and improvement method than offline-based universities. This study examined the main factors affecting dropout during the first semester of 2017 and 2018 A Cyber University. This included management and counseling factors by the 'Decision Tree Analysis Model'. The Management and counseling factors were presented as a decision-making method and weekly methods. As a result, a 'Dropout Improvement Model' was implemented and applied to cyber-university freshmen in the first semester of 2019. The dropout-rate in freshmen applying the 'Dropout Improvement Model' decreased by 4.2%, and the learning-persistence rate increased by 11.4%. This study applied a questionnaire survey, and the cyber-university students LMS (Learning Management System) learning results were analyzed objectively. On the other hand, the students' learning results were analyzed quantitatively, but qualitative analysis was not reflected. Nevertheless, further study is necessary. The 'Dropout Improvement Model' of this study will be applied to help improve the dropout rate and learning persistence rate of cyber-university.

• Journal of Digital Contents Society
• /
• v.19 no.6
• /
• pp.1115-1121
• /
• 2018

In this paper, we have studied a data analysis method by deep learning to predict learning achievements based on accumulated data in cyber university learning management system. By predicting learner's academic achievement, it can be used as a tool to enhance learner's learning and improve the quality of education. In order to improve the accuracy of prediction of learning achievements, the autoencoder based attendance prediction method is developed to improve the prediction performance and deep learning algorithm with ongoing evaluation metrics and predicted attendance are used to predict the final score. In order to verify the prediction results of the proposed method, the final grade was predicted by using the evaluation factor attendance data of the learning process. The experimental result showed that we can predict the learning achievements in the middle of semester.

• Journal of Internet Computing and Services
• /
• v.10 no.5
• /
• pp.135-143
• /
• 2009

The purpose of this study is to examine the variables related to the students' e-learning participation that have an effect on learning achievement in e-learning environment of cyber universities. Based on the related research, the followings are derive. First, students' attendance and participation in discussion showed higher correlation with the learning achievement than other participation variables. However, the total studying time in online classes showed lower correlation with the learning achievement. Second, the variables that have an effect on the learning achievement were in the order of students' attendance, participation in discussion, access frequency to online classes, learning progress and number of data uploads. Third, by the learners' background, the difference among the variables that have an effect on learning achievement were found. Based on the results above, this study suggests considerations about participation variables to enhance the learning achievement in cyber universities.