• The Journal of Society for e-Business Studies
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• v.6 no.3
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• pp.115-126
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• 2001

In constructive view, acquiring knowledge is made by experiences among members or elements. The knowledge in e-learning system can be extended up to knowledge of teachers and knowledge. of operating managers. We have many difficult problems to develop and manage e-1earning system because demanders on e-learning system have various. requirements. In traditional education system demanders are learners but in constructive view demanders can be extended to learners and teachers, operating mangers on e-learning system., In this study, we design and implement e-learning system named kedu V.1. Kedu V.1 is developed considering interactions of extended demanders of e-learning system in constructive view. And this system based on Linux operation system and MySQL, PHP. Also this system have efficient transplantation and portability capabilities and reduced cost and labor in implementation of real e-learning system

• Educational Technology International
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• v.7 no.2
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• pp.23-44
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• 2006

As the educational paradigm has shifted from the traditional knowledge oriented instruction learning to the knowledge product oriented instructional learning, the development of student's creativity becomes one of the most important educational goals, because the ability that can produce the knowledge creatively is required in the digital information knowledge based society. The purpose of this study is to make a basic direction and strategy for the instructional design to develop an on and off line blended instructional design which will help a learning community to be a more collaborative and creative learning community. This research has investigated the concept and the characteristics of collaborative creativity and creative problem solving as the theoretical basis of the design. After that, on the basis of the theories connected with the collaborative creativity theory, the direction and the strategies for the development of collaborative creativity was designed. The design was applied into the real learning community and finally proved the effectiveness of the learning model for the development of the collaborative creativity by the quantitative evaluation.

• Journal of Korean Elementary Science Education
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• v.25 no.2
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• pp.167-178
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• 2006

The purpose of this study was to explore characteristics of sixth grade students' opistemological beliefs in science and the relationship to learner's characteristics: learning motivation, learning strategies, and logical thinking. The subjects were 265 sixth graders and data was collected through two types of questionnaires, translated and modified by researchers: opistemological beliefs regarding science, learning motivation & strategies. The results of this study were as follows. The students believed that the goals of science were related to activations such as 'Science is experiment', or 'Science is invention: These beliefs were connected with the emphasis of science classes or the focus of the science curriculum. However, the students' beliefs related to the changeability of science knowledge, the source of science knowledge, and the role of experiments in developing knowledge were oriented to modern opistemological views. Moreover, the beliefs were meaningfully related to students' characteristics: learning motivation, learning strategies, and logical thinking. Among the students' characteristics, logical thinking was especially related to all of the factors of students' beliefs: the changeability of science knowledge, the source of science knowledge, and the role of experiments in developing knowledge. However, the students who believed that scientific knowledge came from scientists, science teachers, or science textbooks had high levels of self-efficacy. Therefore, the belief that scientific knowledge is formed by self-discovery, in order to generate high self-efficacy, needs to be encouraged. From the results, it is possible to check the orientation of current science education based on the students' opistemological beliefs. In addition, the resources can be accumulated for persevering in our efforts to achieve a positive orientation for science education.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.764-770
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• 2004

This paper presents a hybrid data mining mechanism to extract expert knowledge from historical data and extend expert systems' reasoning capabilities by using fuzzy neural network (FNN)-based learning & rule extraction algorithm. Our hybrid data mining mechanism is based on association rule extraction mechanism, FNN learning and fuzzy rule extraction algorithm. Most of traditional data mining mechanisms are depended ()n association rule extraction algorithm. However, the basic association rule-based data mining systems has not the learning ability. Therefore, there is a problem to extend the knowledge base adaptively. In addition, sequential patterns of association rules can`t represent the complicate fuzzy logic in real-world. To resolve these problems, we suggest the hybrid data mining mechanism based on association rule-based data mining, FNN learning and fuzzy rule extraction algorithm. Our hybrid data mining mechanism is consisted of four phases. First, we use general association rule mining mechanism to develop an initial rule base. Then, in the second phase, we adopt the FNN learning algorithm to extract the hidden relationships or patterns embedded in the historical data. Third, after the learning of FNN, the fuzzy rule extraction algorithm will be used to extract the implicit knowledge from the FNN. Fourth, we will combine the association rules (initial rule base) and fuzzy rules. Implementation results show that the hybrid data mining mechanism can reflect both association rule-based knowledge extraction and FNN-based knowledge extension.

• Journal of Information Science Theory and Practice
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• v.10 no.3
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• pp.24-39
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• 2022

Online learning is becoming ubiquitous worldwide because of its accessibility anytime and from anywhere. However, it cannot be successfully implemented without understanding constructs that may affect its adoption. Unlike previous literature, this research extends the Unified Theory of Acceptance and Use of Technology with three well-known theories, namely compatibility, online self-efficacy, and knowledge sharing and acquisition to examine online learning adoption. A total of 264 higher education students took part in this research. Partial Least Squares-Structural Equation Modeling was used to evaluate the proposed theoretical model. The findings suggested that performance expectancy and compatibility were significant predictors of behavioral intention, whereas behavioral intention, facilitating conditions, and compatibility had a significant and direct effect on online learning's actual use. The results also showed that knowledge acquisition, knowledge sharing, and online self-efficacy were determinates of performance expectancy. Finally, online self-efficacy was a predictor of effort expectancy. The proposed model achieved a high fit and explained 47.7%, 75.1%, 76.1%, and 71.8% of the variance of effort expectancy, performance expectancy, behavioral intention, and online learning actual use, respectively. This study has many theoretical and practical implications that have been discussed for further research.

• Asia pacific journal of information systems
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• v.25 no.4
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• pp.642-661
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• 2015

Interpersonal trust has become essential for online communities because people have managed to be in a situation without face-to-face encounters. To identify the structural relationships between interpersonal trust and learning performance, we analyzed the relationship between two types of trust, namely, cognitive and affective, as well as two dimensions of learning performance, namely, learning satisfaction and knowledge application. We also identified the moderating role of social norms in the relationship between trust and learning performance. Results of analysis are as follows. First, cognitive trust significantly affected the two dimensions of performance. Second, affective trust exhibited a significant effect on learning satisfaction, but did not affect knowledge application. Third, the relationships between the two performance factors were significant and direct. Lastly, social norms appeared to moderate the effects of cognitive trust on knowledge application and affective trust on satisfaction. These findings suggest that organizations, which would like to optimize task-oriented performance of their learning communities, should consider linking strategies between community satisfaction and practical knowledge application.

• Research in Mathematical Education
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• v.25 no.1
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• pp.1-20
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• 2022

Collaborative learning has been highlighted as an effective method of teachers' professional development in various studies. To disclose teachers' discourse threads in the process of collaborative learning for developing their knowledge, this paper adopted two methods including "content analysis" and "time-sequential analysis" of learning analytics. Such analyses were implemented for mining teachers' updated knowledge and the discourse threads in the discussion during collaborative learning. The materials for analysis involved two aspects: one was from the video-taped lesson observation reports written by teachers before and after discussing, and the other was from their discourses during the discussion process. The results proved that teachers' knowledge for teaching the centroid of a triangle was updated in the collaborative learning period, and also revealed the discourse threads of teachers' collaboration contained "requesting information or opinions", "building on ideas", and "providing evidence or reasoning", with the emphasis on "challenging ideas or re-focusing talk"

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.11
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• pp.381-389
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• 2017

Recently, due to the continuous changes in the business environment and increased competition, enterprises are introducing technology management to increase their value and enhance their competitiveness. In this study, based on the structural equations model, we investigated the effects of the core success factors of knowledge management on job fidelity and organizational performance and the mediating effects of organizational learning. In addition, organizational culture and information technology and process were assumed to be the key factors affecting knowledge management, and organizational learning was represented by experimental learning, indirect learning, and shared learning. As a result, it was found that knowledge management positively affects business fidelity and organizational performance and, even when it is mediated by organizational learning, it positively affects business fidelity and organization performance directly or indirectly. Therefore, we were able to confirm the importance of utilizing knowledge management in companies and to suggest an appropriate application scope for applying knowledge management and organizational learning.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1309-1314
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• 2003

If an agent has a learning ability using previous knowledge, then it is expected that the agent can speed up learning by interacting with environment. In this paper, we present an improved reinforcement learning algorithm using domain knowledge which can be represented by problem-independent features and their classifiers. Here, neural networks are employed as knowledge classifiers. To show the validity of our proposed algorithm, computer simulations are illustrated, where navigation problem of a mobile robot and a micro aerial vehicle(MAV) are considered.

• Asia pacific journal of information systems
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• v.6 no.2
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• pp.77-105
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• 1996

The accuracy of knowledge is a major concern for expert system developers and users. Machine learning approaches have recently been found to be useful in knowledge acquisition for expert systems. However, the accuracy of concept acquired from machine learning could not be analyzed in most cases. In this paper we develop a comprehensive knowledge error analysis methodology for practical use of expert systems. Decision tree induction is an important type of machine learning method for business expert systems. Here we start to analyze with knowledge acquired from decision tree induction method, and extend the results to develop error analysis methodology for general machine learning methods. We give several examples and illustrations for these results. We also discuss the applicability of these results to multistrategy learning approaches.