• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.189-194
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• 2013

Social learning is a new study model of future knowledge information society. In different existing study, it lay stress on individual activity and collaborative study with others. It is useful to apply social media services to build social learning platform for collaborative study. In my paper, after existing social media services and social platforms are investigated and analyzed, an effective social learning platform applyng social media services is presented. Also differences and superiority compared to other social platforms is presented through new social learning platform architecture and screen design.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.861-880
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• 2023

Semantic segmentation of road scene is the key technology of autonomous driving, and the improvement of convolutional neural network architecture promotes the improvement of model segmentation performance. The existing convolutional neural network has the simplification of learning knowledge and the complexity of the model. To address this issue, we proposed a road scene semantic segmentation algorithm based on multi-task collaborative learning. Firstly, a depthwise separable convolution atrous spatial pyramid pooling is proposed to reduce model complexity. Secondly, a collaborative learning framework is proposed involved with saliency detection, and the joint loss function is defined using homoscedastic uncertainty to meet the new learning model. Experiments are conducted on the road and nature scenes datasets. The proposed method achieves 70.94% and 64.90% mIoU on Cityscapes and PASCAL VOC 2012 datasets, respectively. Qualitatively, Compared to methods with excellent performance, the method proposed in this paper has significant advantages in the segmentation of fine targets and boundaries.

• Journal of Digital Convergence
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• v.16 no.10
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• pp.1-9
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• 2018

The purpose of this study is to examine the concepts and types of cross-cultural collaborative online learning that enhance the utilization of advanced ICT in education and contribute to the promotion of educational exchanges between countries, and suggest exchange learning design models necessary for the active introduction. For this study, previous studies related to cross-cultural collaborative online learning were examined. As a result, cross-cultural collaborative online learning is an educational method based on constructivism that explore and construct knowledge by interacting and collaborating with students, teachers, and field experts who are linguistically and culturally heterogeneous based on advanced ICT. The type of cross-cultural collaborative online learning could be divided into synchronous exchange learning centered on remote video classes and asynchronous exchange learning centered on website based tasks. A PPIE learning design model considering the characteristics of each type is presented.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.12
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• pp.2657-2664
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• 2012

Web-based learning receives much attention as an effective learning method because users can use the learning service at any time from any space. A learning management system(LMS) provides online educational environment among teachers and students, supporting various facilities to deliver educational contents. Since most of the existing LMSs support one-way or limited two-way teaching services among teachers and students, there are a lot of difficulties in performing collaboration among students and/or collaboration among teachers and students. In this paper, we describe the development of collaborative workspaces which provides effective collaborative educational environment on Moodle which is widely accepted as a typical LMS. Through the provided various types of collaborative workspaces, users can easily perform group activities, sharing educational with appropriate access control mechanism.

• Journal of The Korean Association of Information Education
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• v.17 no.3
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• pp.305-316
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• 2013

Despite the potential implications of Facebook use, there is a distinct lack of empirically derived theory for designing learning environment. This may be because Facebook is a social tool and there has been limited opportunity for exploratory research regarding Facebook based learning. Therefore, the purpose of this study is to investigate learning presence factors affecting learning outcomes in Facebook-based collaborative learning. Forty two college students participated in the Facebook-based collaborative learning activity, and the data from thirty nine were used for step-wise multiple regression analysis. In addition focus group interview was conducted to examine learning presence of Facebook-based collaborative learning. The results reported that cognitive presence predicted significantly learning outcomes, however, social and emotional presence did not predict learning outcomes. The implication of this study and future research were discussed in this research.

• Journal of Convergence for Information Technology
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• v.10 no.1
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• pp.120-127
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• 2020

The purpose of this study was to examine related variables that improve learning outcomes in collaborative learning. Based on literature reviews, emotional intelligence was used as a variable of personal character, communication ability and collective intelligence were used as variables in learning process, and learning satisfaction, and persistence were used as variables of learning outcomes. Data were collected from 3,475 students at A university, and were analyzed using structural equation modeling. The results of this study are as follows: First, it turned out that emotional intelligence had a significant and positive impact on communication ability, collective intelligence, learning satisfaction, and persistence. Second, communication ability influenced collective intelligence and persistence positively. Third, collective intelligence influenced learning satisfaction and persistence positively. Fourth, learning satisfaction had a significant and positive impact on persistence. These findings offer basic data for collaborative learning by revealing the structural relationships among related variables that improve learning outcomes in collaborative learning of college students.

• Korean Medical Education Review
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• v.22 no.2
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• pp.144-144
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• 2020

• The Journal of Industrial Distribution & Business
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• v.10 no.2
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• pp.25-37
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• 2019

Purpose - The goal of this study is to analyze the differences in education performances between students of the government's financial support program and those who do not receive support at a local university in Korea. Research design, data, and methodology - The questionnaire used was NASEL. NASEL is considered a highly suitable survey tool for professors, courses, and performances in Korean universities. The 290 students who participated and 44 students do not participate in the financial support program were surveyed for 10 days. The characteristics of students were investigated by frequency analysis and technical statistics. The analysis of student collective characteristics used independent t and f-tests,and one-way ANOVA with IBM SPSS Statistics 22.0 for statistical purposes. Results - The p-value of the group receiving financial support and the group without financial support in active-collaborative learning is 0.167. The p-value of the economically supported group and the non-supported group of the faculty-student interaction is 0.281. The confidence coefficient of the active-collaborative learning questionnaire is 0.861. The reliability coefficient of the questionnaire for the faculty-student interaction questionnaire is 0.871. Conclusions - There are no clear differences in active-collaborative learning and faculty-student interaction between participating and non-participating students in the economic program.

• Journal of Information Technology Applications and Management
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• v.29 no.3
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• pp.43-55
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• 2022

In this study, with the goal of developing a deep learning-based product recommendation model for effective matching of influencers and products, a deep learning model with a collaborative filtering model combined with generalized matrix decomposition(GMF), a collaborative filtering model based on multi-layer perceptron (MLP), and neural collaborative filtering and generalized matrix Factorization (NeuMF), a hybrid model combining GMP and MLP was developed and tested. In particular, we utilize one-class problem free boosting (OCF-B) method to solve the one-class problem that occurs when training is performed only on positive cases using implicit feedback in the deep learning-based collaborative filtering recommendation model. In relation to model selection based on overall experimental results, the MLP model showed highest performance with weighted average precision, weighted average recall, and f1 score were 0.85 in the model (n=3,000, term=15). This study is meaningful in practice as it attempted to commercialize a deep learning-based recommendation system where influencer's promotion data is being accumulated, pactical personalized recommendation service is not yet commercially applied yet.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3730-3744
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• 2020

This study proposes a deep learning-based evolutionary recommendation model for heterogeneous big data integration, for which collaborative filtering and a neural-network algorithm are employed. The proposed model is used to apply an individual's importance or sensory level to formulate a recommendation using the decision-making feedback. The evolutionary recommendation model is based on the Deep Neural Network (DNN), which is useful for analyzing and evaluating the feedback data among various neural-network algorithms, and the DNN is combined with collaborative filtering. The designed model is used to extract health information from data collected by the Korea National Health and Nutrition Examination Survey, and the collaborative filtering-based recommendation model was compared with the deep learning-based evolutionary recommendation model to evaluate its performance. The RMSE is used to evaluate the performance of the proposed model. According to the comparative analysis, the accuracy of the deep learning-based evolutionary recommendation model is superior to that of the collaborative filtering-based recommendation model.