• Proceedings of the Korea Society of Design Studies Conference
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• 2002.11b
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• pp.28-29
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• 2002

• Journal of Convergence for Information Technology
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• v.10 no.9
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• pp.173-182
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• 2020

In recent years, collaborative learning in university courses has been emphasized in order to improve collective intelligence. Based on literature reviews, individual creativity was used as a variable of personal characteristic, team trust and collective efficacy were used as variables of teams to see the relationship with collective intelligence as a variable of learning outcome. Data were collected from 770 students from A University in Gyeonggi-do, H University in the Daejeon, and K University in Chungcheong-do, and analyzed by using structural equations modeling. As results, individual creativity had significant influence on collective efficacy and collective intelligence. Team trust also had significant influence on collective efficacy and collective intelligence. In addition, collective efficacy had a positive effect on collective intelligence. This study will be able to utilize basic data for establishing instructional design and strategies of collaborative learning in the universities.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.4
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• pp.11-17
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• 2010

In this paper, we propose a recommendation system for supporting self-directed programming language education. The system is a recommendation system using collaborative filtering based on learners' level and stage. In this study, we design a recommendation system which uses collaborative filtering based on learners' profile of their level and correlation profile between learning topics in order to increase self-directed learning effects when students plan their learning process in e-learning environment. This system provides a way for solving a difficult problem, that is providing programming problems based on problem solving ability, in the programming language education system. As a result, it will contribute to improve the quality of education by providing appropriate programming problems in learner"s level and e-learning environment based on teaching and learning method to encourage self-directed learning.

• Journal of the Korea Society of Computer and Information
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• v.15 no.8
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• pp.51-58
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• 2010

Programming education needs learning which is adjusted individual learners' level of their learning abilities. Recommendation system is one way of implementing personalized service. In this research, we propose recommendation method which learning items are recommended for individual learners' learning in web-based programming education environment by. Our proposed system for leveled programming education provides appropriate programming problems for a certain learner in his learning level and learning scope employing collaborative filtering method using learners' profile of their level and correlation profile between learning topics. As a result, it resolves a problem that providing appropriate programming problems in learner's level, and we get a result that improving leaner's programming ability. Furthermore, when we compared our proposed method and original collaborative filtering method, our proposed method provides the ways to solve the scalability which is one of the limitations in recommendation systems by improving recommendation performance and reducing analysis time.

• Educational Technology International
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• v.10 no.1
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• pp.1-23
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• 2009

Learners are often posited in a paradoxical situation where they are not fully involved in decision making processes on how to learn, in designing their tools. Cognitive artifacts in e-learning are supposed to effectively support learner-centered e-learning. The purpose of the study is to analyze cases of cognitive artifacts and to inquire those design principles for facilitating the learner-centered e-learning. Four research questions are suggested: First, it will be analyzed the characteristics of learners with respect to design of cognitive artifacts for supporting the learner-centered e-learning. Second, characteristics of four cases to design cognitive artifacts in learner-centered e-learning environment are analyzed. Third, it will be suggested the appropriate design principles of cognitive artifacts to facilitating learner-centered learning in e-learning environment. Four cases of cognitive artifacts design in learner-centered e-learning was identified as follows: Wiki software as cognitive artifacts in computer-supported collaborative learning; 'Play Around Network (PAN)' as cognitive artifact to monitor learning activities in knowledge community; Knowledge Forum System (KFS) as a cognitive artifact in knowledge building; cognitive artifacts in Courses-as-seeds applied meta-design. Five design principles are concluded as follows: Promoting externalization of cognitive artifacts to private media; Helping learners to initiate their learning processes; Encouraging learners to make connections with other learners' knowledge building and their cognitive artifacts; Promoting monitoring of participants' contributions in collaborative knowledge building; Supporting learners to design their cognitive artifacts.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.346-357
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• 2019

The purpose of this study was to examine the effects of flipped learning and explore the learners' experiences. Data were collected from 64 students who participated in flipped learning for 7 weeks at N university. The results were as follows. First, after applying flipped learning, learners feel more comfortable learning together and prefer collaborative learning. Second, flipped learning had no significant effects on learner's overall metacognition, but it had positive effects on the awareness and cognitive strategies. Third, flipped learning had no significant effects on academic self-efficacy, but it positively affected the task difficulty preference and confidence of learners who had a lower level of collaborative tendencies. Fourth, flipped learning had no significant effects on SDL ability, but it positively affected the learning plan of learners who had a higher level of collaborative tendencies. Fifth, learners' class satisfaction of flipped learning was generally very high. We suggested a policy, instructional design and strategies for effective implementation of flipped learning.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.1075-1087
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• 2020

Aviation ICT technology is a convergence technology between aviation and electronics, and has a wide variety of applications, including navigation and education. Among them, in the field of aerial pilot training, there are many problems such as the possibility of accidents during training and the lack of coping skills for various situations. This raises the need for a simulated pilot training system similar to actual training. In this paper, pilot training data were collected in pilot training system using VR/AR to increase immersion in flight training, and Customized Pilot Training Platform with Collaborative Deep Learning in VR/AR Environment that can recommend effective training courses to pilots is proposed. To verify the accuracy of the recommendation, the performance of the proposed collaborative deep learning algorithm with the existing recommendation algorithm was evaluated, and the flight test score was measured based on the pilot's training data base, and the deviations of each result were compared. The proposed service platform can expect more reliable recommendation results than previous studies, and the user survey for verification showed high satisfaction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2399-2413
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• 2021

A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the cold-start problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.1055-1060
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• 2007

최근 사회적 소프트웨어(Social Software)의 급격한 발전은 미래의 지식근로자가 될 넷제너레이션의 학습 방법에 큰 영향을 줄 것임에 틀림없다. 이러한 변화에 맞추어 대학 교육에서도 오늘날의 학생들이 지식을 창출하고 공유하는 경험을 하게 하는 사회적 소프트웨어를 교육에 활용하는 교수법이 확산 ㄷ히고 있다. 구체적으로 블로그(Blog)와 위키(Wiki)와 같은 사회적 소프트웨어를 사용하는 교육 방법에 대한 관심이 높아지고 있다. 본 논문에서는 위키(Wiki), 블로그(Blog)와 같은 사회적 소프트웨어를 사용하여 실시하는 협업 학습(Collaborative Learning)의 이론적 배경과 운영 경험을 소개한다.

• The Journal of Korean Academic Society of Nursing Education
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• v.20 no.4
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• pp.577-586
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• 2014

Purpose: This study investigates patterns of small group interaction and examines the influence among graduate nursing students of online collaborative learning strategies on small group interaction patterns, task performance and learning attitude in web-based team learning environments. Method: To analyze patterns of small group interaction, group discussion dialogues were reviewed by two instructors. Groups were divided into two categories depending on the type of feedback given (passive or active). For task performance, evaluation of learning processes and numbers of postings were examined. Learning attitude toward group study and coursework were measured via scales. Results: Explorative interactions were still low among graduate nursing students. Among the students given active feedback, considerable individual variability in interaction frequency was revealed and some students did not show any specific type of interaction pattern. Whether given active or passive feedback, groups exhibited no significant differences in terms of task performance and learning attitude. Also, frequent group interaction was significantly related to greater task performance. Conclusion: Active feedback strategies should be modified to improve task performance and learning attitude among graduate nursing students.