• Knowledge Management Research
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• v.19 no.3
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• pp.173-187
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• 2018

This study was aimed at suggesting an integrative framework for creating collective intelligence and enhancing group performance after reviewing previous studies including those related to learning organizations, organizational learning, knowledge management, and collective intelligence. In the first, we examined that the similarities and differences between collective intelligence and other similar concepts, such as learning organizations, organizational learning, and knowledge management. Next, an integrative framework for creating collective intelligence and channeling it into strong group performance were suggested. In this process, we reviewed conditions for creating collective intelligence and segmented the major variables as expectancy, valence, and instrumentality, according to Vroom's (1964) expectancy theory. Characteristics of problems and the roles of leaders were respectively considered as valence for inducing collaboration and expectancy for managing probability to achieve goals. Instrumental factors were also adopted from conditions for creating group intelligence suggested from several researchers, such as creativity, openness, willingness for working together, horizontal communication, centralization in decision making, and building effective information and communication technology system and active usage of it. We discussed two potentially disputable matters about the scope and level of collective intelligence and group performance and suggest several theoretical and practical implications in the Discussion.

• 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 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 System Dynamics Review
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• v.7 no.2
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• pp.133-148
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• 2006

This study suggests a Causal Loop Diagram(CLD) of causality mechanism which are integrating matters of localization, networking, embeddedness & institutional thickness and collective learning. These five factors(localization, networking, embeddedness & institutional thickness, collective learning, innovative synergy) have been studied and proofed Also this study suggest a model of industry cluster based on holistic and global system thinking rather than local and linear thinking.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.117-129
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• 2024

This paper introduces collective navigation through a narrow gap using a curriculum-based deep reinforcement learning algorithm for a swarm of unmanned aerial vehicles (UAVs). Collective navigation in complex environments is essential for various applications such as search and rescue, environment monitoring and military tasks operations. Conventional methods, which are easily interpretable from an engineering perspective, divide the navigation tasks into mapping, planning, and control; however, they struggle with increased latency and unmodeled environmental factors. Recently, learning-based methods have addressed these problems by employing the end-to-end framework with neural networks. Nonetheless, most existing learning-based approaches face challenges in complex scenarios particularly for navigating through a narrow gap or when a leader or informed UAV is unavailable. Our approach uses the information of a certain number of nearest neighboring UAVs and incorporates a task-specific curriculum to reduce learning time and train a robust model. The effectiveness of the proposed algorithm is verified through an ablation study and quantitative metrics. Simulation results demonstrate that our approach outperforms existing methods.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.179-192
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• 2014

Keeping in line with the recent changes in the information technology environment, the online learning environment that supports multiple users' participation such as MOOC (Massive Open Online Courses) has become important. One of the largest professional associations in Information Technology, IEEE Computer Society, announced that "Supporting New Learning Styles" is a crucial trend in 2014. Popular MOOC services, CourseRa and edX, have continued to build active learning environment with a large number of lectures accessible anywhere using smart devices, and have been used by an increasing number of users. In addition, collaborative web services (e.g., blogs and Wikipedia) also support the creation of various user-uploaded learning materials, resulting in a vast amount of new lectures and learning materials being created every day in the online space. However, it is difficult for an online educational system to keep a learner' motivation as learning occurs remotely, with limited capability to share knowledge among the learners. Thus, it is essential to understand which materials are needed for each learner and how to motivate learners to actively participate in online learning system. To overcome these issues, leveraging the constructivism theory and collective intelligence, we have developed a social bookmarking system called WeStudy, which supports learning material sharing among the users and provides personalized learning material recommendations. Constructivism theory argues that knowledge is being constructed while learners interact with the world. Collective intelligence can be separated into two types: (1) collaborative collective intelligence, which can be built on the basis of direct collaboration among the participants (e.g., Wikipedia), and (2) integrative collective intelligence, which produces new forms of knowledge by combining independent and distributed information through highly advanced technologies and algorithms (e.g., Google PageRank, Recommender systems). Recommender system, one of the examples of integrative collective intelligence, is to utilize online activities of the users and recommend what users may be interested in. Our system included both collaborative collective intelligence functions and integrative collective intelligence functions. We analyzed well-known Web services based on collective intelligence such as Wikipedia, Slideshare, and Videolectures to identify main design factors that support collective intelligence. Based on this analysis, in addition to sharing online resources through social bookmarking, we selected three essential functions for our system: 1) multimodal visualization of learning materials through two forms (e.g., list and graph), 2) personalized recommendation of learning materials, and 3) explicit designation of learners of their interest. After developing web-based WeStudy system, we conducted usability testing through the heuristic evaluation method that included seven heuristic indices: features and functionality, cognitive page, navigation, search and filtering, control and feedback, forms, context and text. We recruited 10 experts who majored in Human Computer Interaction and worked in the same field, and requested both quantitative and qualitative evaluation of the system. The evaluation results show that, relative to the other functions evaluated, the list/graph page produced higher scores on all indices except for contexts & text. In case of contexts & text, learning material page produced the best score, compared with the other functions. In general, the explicit designation of learners of their interests, one of the distinctive functions, received lower scores on all usability indices because of its unfamiliar functionality to the users. In summary, the evaluation results show that our system has achieved high usability with good performance with some minor issues, which need to be fully addressed before the public release of the system to large-scale users. The study findings provide practical guidelines for the design and development of various systems that utilize collective intelligence.

• Journal of The Korean Association of Information Education
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• v.12 no.3
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• pp.355-363
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• 2008

In order to enhance the effect of Problem-based Learning, the role of scaffold as a learning support strategy is necessary. Collective intelligence provides scaffolding in the sense that it integrates users' knowledge, information, experiences, values, etc. Based on these factors, collective intelligence determines the direction of behavior, revises the direction continuously, and provides problem-solving methods. Teaching and learning situations emphasize learners' initiative, voluntary, and active participation. Thus, this study was conducted to find out if collective intelligence can be an effective and attractive alternative of learning strategy. Specifically, this study purposed to examine how collective intelligence performs the role of scaffold on the Web and what types of scaffolding are provided to learners. According to the results of this study, collective intelligence had a positive effect on learners' learning attitude, confidence, interest, etc. in the affective aspect, but its effect on the cognitive aspect was different according to learners' school year and learning level. Because collective intelligence had a positive effect on learners, we identified scaffolding types explanation, suggestion of direction, illustration and feedback in the cognitive aspect, and positive response and encouragement in the affective aspect.

• The Journal of the Korea Contents Association
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• v.15 no.2
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• pp.529-541
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• 2015

Collaborative learning of collective intelligence-based model is also ideal in higher education did not yet consensus still in the theoretical level. To become collective intelligence-based collaborative learning is to mobilize the competence of the various members should be promoted as much as possible with their own services designed to actively participate in and contribute to the goals of the joint. Is still based collaborative learning model of collective intelligence, which does the actual model is not developed in education is a key program in creative fusion judge called talent. The evolution of the main features of the house just in shaping the content of a modern lecture geureohagi need to check from time to time to see and pay attention. As part of this study, attempts were associated with the tvN planning and attention to trying connector Executive Lecture show "Creative Club 199" content. Well oriented intention to converge the needs of the times, but it is even more compelling naeeotda implement the collective intelligence based on 'how' the reality is that together with the participants.

• Journal of Radiation Protection and Research
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• v.48 no.1
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• pp.20-27
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• 2023

Background: As the number of nuclear facilities nearing their pre-determined design life increases, demand is increasing for technology and infrastructure related to the decommissioning and decontamination (D&D) process. It is necessary to consider the nature of the dismantling environment constantly changing and the worker doing new tasks. A method was studied that can calculate the effect of learning and the change in work time on the work process, according to the learning-forgetting curve model (LFCM). Materials and Methods: The LFCM was analyzed, and input values and scenarios were analyzed for substitution into the D&D process of a nuclear facility. Results and Discussion: The effectiveness and efficiency of the training were analyzed. It was calculated that skilled workers can receive a 16.9% less collective radiation dose than workers with only basic training. Conclusion: Using these research methods and models, it was possible to calculate the change in the efficiency of workers performing new tasks in the D&D process and the corresponding reduction in the work time and collective dose.

• Educational Technology International
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• v.11 no.2
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• pp.105-121
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• 2010

Collaborative learning encourages the use of high-level cognitive strategies, critical thinking, and interpersonal relationships. Despite these advantages, most instructors reveal the difficulties of using project-based collaborative learning; a common problem is the failure of the group to work effectively together. Thus, this study attempted to provide practical advice on group composition with self-regulation. In a college course, 31 groups with 129 students were asked to discuss and prepare the final presentation material and present it together as a collaborative work. All students' self-regulation skills were measured at the beginning of the semester, and the collective self-regulation was computed as an average of the individual scores of each group. The results of regression analysis indicate that the group's collective self-regulation shows a highly significant positive effect on group performance and satisfaction, as self-regulation predicts individual academic performance. The results also show that there is a significant positive relationship between students' self-regulation and participation in group work.