• The Journal of Korean Association of Computer Education
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• v.9 no.2
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• pp.27-36
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• 2006

This study intends to present essential models for collaborative learning in e-learning environment as well as to analyze learning behavior elements appearing in collaborative learning activities. In order to achieve goal of the study, the researchers analyzed existing cooperative learning models for face-to-face classroom, collaborative activity models based on instructional theory, and the structures and activities elements of learning community and collaborative activity models focusing on e-learning environment. As a result of the study, the researchers produced a generalizable collaborative learning model for e-learning which include general collaborative learning model, and further analyzed specific learning behaviors performed by learners while they proceed in this model based learning processes. The adequacy of this model and reliability of learning behavior elements were tested through experts' review meetings. The research result, suggesting generalizable collaborative learning model as well as learning behaviors elements which might occur within e-learning based collaborative learning, might work as a foundational model for software infrastructure and e-learning solution business. Moreover, its value might be maximized if its being used for enhancing learning content interoperability and reuse as well as for establishing international standardization for collaborative technology.

• International Journal of Advanced Culture Technology
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• v.9 no.1
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• pp.168-176
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• 2021

This study developed and evaluated a learning model to improve collaborative problem-solving skills for nursing students taking physiology courses. This one-group pretest-posttest design used the jigsaw cooperative learning method on 30 nursing students from one local university. We analyzed the effect of a cooperative problem- solving learning model using SPSS 21.0 to compare changes in the students' collaborative self-efficacy, problem-solving abilities, and team-member exchange. As a result, the participants showed significant increases in collaborative self-efficacy, problem-solving ability, and team-member exchange after experiencing cooperative problem- solving learning model. Therefore, we will help nursing students improve their communication skills by enhancing their collaborative self-efficacy and help them solve problems effectively in conflict situations.

• Journal of Game and Entertainment
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• v.2 no.3
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• pp.8-14
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• 2006

The social interest for collaborative learning and educational game has been increased. In this paper, we investigates the educational value of collaborative learning and game. Based on this investigation, we propose an educational on-line game model for collaborative learning. Although the proposed model is still conceptual design, it sufficiently shows that on-line RPG game can be a good collaborative learning method for young children. In detail, we speculate about the conventional educational method performed in normal school. And then, we describes the elements of a computer game which complies with the real collaborative learning program.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2310-2332
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• 2020

In recent years, deep learning techniques have achieved tremendous successes in natural language processing, speech recognition and image processing. Collaborative filtering(CF) recommendation is one of widely used methods and has significant effects in implementing the new recommendation function, but it also has limitations in dealing with the problem of poor scalability, cold start and data sparsity, etc. Combining the traditional recommendation algorithm with the deep learning model has brought great opportunity for the construction of a new recommender system. In this paper, we propose a novel collaborative recommendation model based on auxiliary stacked denoising autoencoder(ASDAE), the model learns effective the preferences of users from auxiliary information. Firstly, we integrate auxiliary information with rating information. Then, we design a stacked denoising autoencoder based collaborative recommendation model to learn the preferences of users from auxiliary information and rating information. Finally, we conduct comprehensive experiments on three real datasets to compare our proposed model with state-of-the-art methods. Experimental results demonstrate that our proposed model is superior to other recommendation methods.

• Education of Primary School Mathematics
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• v.5 no.2
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• pp.71-94
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• 2001

The purposes of this study were to design small group collaborative learning models for developing the creativity and to analyze the effects on applying the models in mathematics teaching and loaming. The meaning of open education in mathematics learning, the relation of creativity and inquiry learning, the relation of small group collaborative learning and creativity, and the relation of assessment and creativity were reviewed. And to investigate the relation small group collaborative learning and creativity, we developed three types of small group collaborative learning model- inquiry model, situation model, tradition model, and then conducted in elementary school and middle school. As a conclusion, this study suggested; (1) Small group collaborative learning can be conducted when the teacher understands the small group collaborative learning practice in the mathematics classroom and have desirable belief about mathematics instruction. (2) Students' mathematical anxiety can be reduced and students' involvement in mathematics learning can be facilitated, when mathematical tasks are provided through inquiry model and situation model. (3) Students' mathematical creativity can be enhanced when the teacher make classroom culture that students' thinking is valued and teacher's authority is reduced. (4) To develop students' mathematical creativity, the interaction between students in small group should be encouraged, and assessment of creativity development should be conduced systematically and continuously.

• 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.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.05a
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• pp.232-239
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• 2005

Collaborative-filtering-enabled Web sites that recommend books, CDs, movies, and so on, have become very popular on the Internet. Such sites recommend items to a user on the basis of the opinions of other users with similar tastes. This paper discuss an approach to collaborative filtering based on the Simple Bayesian and apply this model to two variants of the collaborative filtering. One is user-based collaborative filtering, which makes predictions based on the users' similarities. The other is item-based collaborative filtering which makes predictions based on the items' similarities. To evaluate the proposed algorithms, this paper used a database of movie recommendations. Empirical results show that the proposed Bayesian approaches outperform typical correlation-based collaborative filtering algorithms.

• 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.

• Journal of Mechanical Science and Technology
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• v.20 no.10
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• pp.1534-1540
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• 2006

Concurrent Engineering (CE) is a popular method employed in product development. It treats the whole product design process by the consideration of product quality, cost, rate of progress, and demands of customers. The development of computer and network technologies provides a strong support to the realization of CE in practice. Aiming at the characteristics of CE and network collaborative design, this paper built network collaborative design system frame. Through the analysis of the network collaborative design modes based on CE, this paper provided a novel network collaborative design integration model. This model can integrate the product design information, design process, and knowledge. Intelligent collaboration was considered in the proposed model. The study showed that the proposed model considered main factors such as information, knowledge, and design process in collaborative design. It has potential application in CE fields.

• Journal of Smart Tourism
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• v.4 no.1
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• pp.19-29
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• 2024

The objective of this article is to create a model of integrated management from the framework modeling of a digital collaborative network supported by knowledge engineering to make heritage site in the Brazil more effective. It is an exploratory and qualitative research with thematic analysis as technique of data analysis from the collaborative network, digital platform, world heritage, and tourism themes. The snowballing approach was chosen, and the mapping and classification of relevant studies was developed with the use of the spreadsheet tool and the Mendeley® software. The results show that the collaborative network model oriented towards strategic objectives should be supported by a digital platform that provides a technological environment that adds functionalities and digital platform services with the integration of knowledge engineering techniques and tools, enabling the discovery and sharing of knowledge in the collaborative network.