• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.265-271
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• 2022

Social media is a form of communication based on the internet to share information through content and images. Their choice of profile images and type of image they post can be closely connected to their personality. The user posted images are designated as personality traits. The objective of this study is to predict five factor model personality dimensions from profile images by using deep learning and neural networks. Developed a deep learning framework-based neural network for personality prediction. The personality types of the Big Five Factor model can be quantified from user profile images. To measure the effectiveness, proposed two models using convolution Neural Networks to classify each personality of the user. Done performance analysis among two different models for efficiently predict personality traits from profile image. It was found that VGG-69 CNN models are best performing models for producing the classification accuracy of 91% to predict user personality traits.

• Journal of Information Technology Services
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• v.7 no.4
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• pp.21-43
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• 2008

This paper develops the research model for the understanding of learner's usage intention in web based collaborative learning(WBCL) system. This model is based on the Davis' Technology Acceptance Model(TAM) and Social Interactivity Theory. Data is collected 225 University students from two different institutions. They were divided into 46 groups and asked to complete an online TOEIC preparation module using WBCL systems over 4 weeks. Data were collected at three points for each participant-before, 3 weeks after, and at the end of the online module. The result show that TAM based Belief factors(Usefulness, Ease of use, Playfulenss) are important determinants of usage intention in WBCL systems. The study also found the external factors of the extended TAM to be subjective norm, leader's enthusiasm in WBCL context.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2026-2033
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• 2023

Due to Thailand's nuclear energy public acceptance problem, the understanding of nuclear energy public perception was the key factor affecting to re-consideration of the nuclear energy program. Thailand Institute of Nuclear Technology and its alliances together developed the classification model for the nuclear energy public perception from the big data comments on social media using Facebook using deep transfer learning. The objective was to insight into the Thailand nuclear energy public perception on Facebook social media platform using sentiment analysis. The supervised learning was used to generate up-to-date classification model with more than 80% accuracy to classify the public perception on nuclear power plant news on Facebook from 2009 to 2022. The majority of neutral sentiments (80%) represented the opportunity for Thailand to convince people to receive a better nuclear perception. Negative sentiments (14%) showed support for other alternative energies due to nuclear accident concerns while positive sentiments (6%) expressed support for innovative nuclear technologies.

• Journal of Creative Information Culture
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• v.4 no.2
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• pp.95-101
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• 2018

As a result of introducing robot assisted learning which utilizes social robots or telepresence robots in language learning or special education, research on technology acceptance model for robot-assisted learning is also being conducted. The unified theory of acceptance and use of technology (UTAUT) model of intelligent robot has been studied, but of tele-operated robot is insufficient. The purpose of this paper is to estimate the UTAUT model by pre-service teachers who experienced telepresence robot-assisted learning that can be done in future school. It is found that the estimated UTAUT model consists of more concise factors than social robots, and the importance of perceived enjoyment is higher. In other words, the pre-service teachers showed significant acceptance of tele-operated robots with enhanced enjoyment composed of its mobility, communication, and touchable appearance of the face and body.

• The Journal of Information Systems
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• v.30 no.2
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• pp.29-56
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• 2021

Purpose Cyber deviance of adolescents has become a serious social problem. With a widespread use of smartphones, incidents of cyber deviance have increased in Korea and both quantitative and qualitative damages such as suicide and depression are increasing. Research has been conducted to understand diverse factors that explain adolescents' delinquency in cyber space. However, most previous studies have focused on a single theory or perspective. Therefore, this study aims to comprehensively analyze motivations of juvenile cyber deviance and to develop a predictive model for delinquent adolescents by integrating four different theories on cyber deviance. Design/methodology/approach By using data from Korean Children & Youth Panel Survey 2010, this study extracts 27 potential factors for cyber deivance based on four background theories including general strain, social learning, social bonding, and routine activity theories. Then this study employs econometric analysis to empirically assess the impact of potential factors and utilizes a machine learning approach to predict the likelihood of cyber deviance by adolescents. Findings This study found that general strain factors as well as social learning factors have positive effects on cyber deviance. Routine activity-related factors such as real-life delinquent behaviors and online activities also positively influence the likelihood of cyber diviance. On the other hand, social bonding factors such as community commitment and attachment to community lessen the likelihood of cyber deviance while social factors related to school activities are found to have positive impacts on cyber deviance. This study also found a predictive model using a deep learning algorithm indicates the highest prediction performance. This study contributes to the prevention of cyber deviance of teenagers in practice by understanding motivations for adolescents' delinquency and predicting potential cyber deviants.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.229-238
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• 2018

The purpose of this study is to investigate the mediating effect of learners' learning flow in the effect of presence on academic achievement in web-based e-learning. For this purpose, this study analyzed the influencing relationship between the each factor based on the structural model with the learning flow as a mediator variable. Based on existing theoretical studies, learning satisfaction and academic achievement, which represent learning outcomes, are set as dependent variables, and teaching presence, cognitive presence, and social presence are set as independent variables. Data collected from a total of 256 e-learning learners were used in the analysis of this study. According to the results of the analysis, teaching presence, cognitive presence, and social presence were found to have a significant effect on academic achievement when a learning flow is a mediator variable. Concretely, teaching presence, cognitive presence, and social presence have a positive effect on the learning flow, while learning flow has a positive effect on learning satisfaction. On the other hand, learning flow has a negative effect on academic achievement. As a result of verifying the mediating effect of learning flow on the relationship between presence, learning satisfaction, and academic achievement, there was meditating effect in the aggregate. This study implies that in order to increase the level of learning satisfaction and academic achievement, it is necessary to make the teaching-learning design in the provision of contents and materials for e-learning so that the learner can feel the presence. The results of this study can be used as a basic data for seeking support and promotion strategies for enhancement of future learning flow and presence.

• Journal of Information Technology Applications and Management
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• v.17 no.1
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• pp.35-54
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• 2010

Why does e-Learning service for individuals remain in the early development stage? To find the answers of this question, we adopted usefulness and intention to continued use as dependent variables based on technology acceptance model and inferred convenience, cost-effectiveness, social presence, interactivity, concentration, and procrastination as independent variables based on literature review and interview with e-Learning users. Convenience and cost-effectiveness of e-Learning tend to enhance usefulness and/or intention to continued use, while lack of social presence, interactivity, and concentration of e-Learning and academic procrastination tend to hinder usefulness and/or intention to continued use. To prove this research model, we used a data set collected from the survey. The respondents of survey were the undergraduate students who used voluntarily e-Learning. Data analysis was conducted using 275 respondents by partial least square. The analysis result of causal relation indicated that convenience and cost-effectiveness influenced both usefulness and intention to continued use, and that cost-effectiveness and concentration influenced only intention to continued use. But, interactivity and procrastination did not influence usefulness and intention to continued use.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.104-110
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• 2020

In the 4th Industrial Revolution, the competitiveness of the software industry is important, and as a solution to fundamentally secure the competitiveness of the software industry, education classes should be provided to educate high quality software personnel in educational institutions. Despite this social situation, software-related classes in universities are largely composed of competitive or individual learning structures. Cooperative learning is a learning model that can complement the problems of competitive and individual learning. Cooperative learning is more effective in improving academic achievement than individual or competitive learning. In addition, most learners have the advantage of having a more desirable self-image by having a successful experience. In this paper, we apply a pair check model, which is a type of cooperative learning, in operating system classes. In addition, the class procedure and instruction plan are designed to apply the pair check model. We analyze the test results to analyze the performance of the cooperative learning model.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.489-493
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• 2024

Happiness index is a measurement system for understanding collective happiness. As values change, studies have been proposed to add the value of behavior to the happiness index. However, there is a lack of studies analyze the relationship using individual emotions. Using a deep learning model, we predicted happiness index using social variables and individual emotional index. First, we collected social and emotional variables from January 2005 to December 2020. Second, we preprocessed the data and identified significant variables. Finally, we trained deep learning-based regression model. Our proposed model was evaluated using 5-fold cross validation. The proposed model showed 90.86% accuracy on test sets. Our model will be expected to analyze the significant factors of country-specific happiness index.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.1
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• pp.87-97
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• 2019

Reaction of people is importantly considered about specific case as a social network service grows. In the previous research on analysis of social network service, they predicted tendency of interesting topic by giving scores to sentences written by user. Based on previous study we proceeded research of sentiment analysis for social network service's sentences, which predict the result as positive or negative for movie reviews. In this study, we used movie review to get high accuracy. We classify the movie review into positive or negative based on the score for learning. Also, we performed embedding and morpheme analysis on movie review. We could predict learning result as positive or negative with a number 0 and 1 by applying the model based on learning result to social network service. Experimental result show accuracy of about 80% in predicting sentence as positive or negative.