• Knowledge Management Research
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• v.12 no.1
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• pp.17-37
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• 2011

This paper is aimed at investigating the influence of the learning organization's structural characteristics, learner's characteristics, and perceived organizational support (POS) on organizational knowledge creation. also the POS is tested as a moderator on the relationship between learner's characteristics including learning goal orientation and learning self-efficacy and organizational knowledge creation. the results are as follows. for main effect hypotheses, both connecting the organization to its environment and establishing systems to capture and share learning system representing learning organization's structural characteristics have significant positive impact on organizational knowledge creation. the POS also has a significant impact on organizational knowledge creation. However, learning goal orientation and learning self-efficacy have not significant impact on organizational knowledge creation. for moderating effect hypothesis, POS moderates the relationship between learning goal orientation and organizational knowledge creation, which means if the POS is high then learning goal orientation has more significant positive impact on it. Based on our findings, we conclude that structural characteristics of learning organization provide organizations with an opportunity of knowledge creation. in particular, interconnectedness of organization with environment and organizational knowledge sharing systems determine the ways of behaving that are related to learning within organizations. however, learner's characteristics did not have a significant effect on organizational knowledge creation, which could be interpreted due to the fact that employees are not motivated to create new knowledge if they are rarely required to involve challenging works, generate new knowledge, or share preexisted knowledge with others.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.31-38
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• 2024

This study proposes hyper-parameter settings for developing a generative AI-based learning support tool to facilitate programming education in online distance learning. We implemented an experimental tool that can set research hyper-parameters according to three different learning contexts, and evaluated the quality of responses from the generative AI using the tool. The experiment with the default hyper-parameter settings of the generative AI was used as the control group, and the experiment with the research hyper-parameters was used as the experimental group. The experiment results showed no significant difference between the two groups in the "Learning Support" context. However, in other two contexts ("Code Generation" and "Comment Generation"), it showed the average evaluation scores of the experimental group were found to be 11.6% points and 23% points higher than those of the control group respectively. Lastly, this study also observed that when the expected influence of response on learning motivation was presented in the 'system content', responses containing emotional support considering learning emotions were generated.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.292-297
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• 2001

In writing learning as a second/foreign language, a learner has to acquire not only lexical and syntactical knowledge but also the skills to choose suitable words for content which s/he is interested in. A learning system should extrapolate learner\\`s intention and give example phrases that concern with the content in order to support this on the system. However, a learner cannot always represent a content of his/her desired phrase as inputs to the system. Therefore, the system should be equipped with a diagnosis function for learner\\`s intention. Additionally, a system also should be equipped with an analysis function to score similarity between learner\\`s intention and phrases which is stored in the system on both syntactic and idiomatic level in order to present appropriate example phrases to a learner. In this paper, we propose architecture of an interactive support method for English writing learning which is based an analogical search technique of sample phrases from corpora. Our system can show a candidate of variation/next phrases to write and an analogous sentence that a learner wants to represents from corpora.

• The Journal of Korean Association of Computer Education
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• v.11 no.5
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• pp.33-45
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• 2008

Field study improves students' capacity for studying and thinking about their surrounding environments. It also develops further interest academic study by allowing them to learn curriculum related materials from actual experience. Moreover, students acquire the capacity for independent and self regulated learning in the course of making efforts to solve problems they face in the environment. Our efforts arc directed at designing and developing a RFID based support system-based on the constructionist's learning theory to help students perform field study more efficiently. The field study support system can be implemented not only in museums but also in botanical gardens, zoos, art galleries, and science centers. Based on the results of the verification at the sample museum we will expand the target locations to implement the field trip support system. We expect that our field study support system will be a catalyst for improving learning in the fields.

• Fourth Industrial Review
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• v.3 no.1
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• pp.13-20
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• 2023

Purpose - By designing a PEF(Personalized Education Feedback) system for real-time prediction of learning achievement and motivation through real-time EEG analysis of learners, this system provides some modules of a personalized adaptive learning system. By applying these modules to e-learning and offline learning, they motivate learners and improve the quality of learning progress and effective learning outcomes can be achieved for immersive self-directed learning Research design, data, and methodology - EEG data were collected simultaneously as the English test was given to the experimenters, and the correlation between the correct answer result and the EEG data was learned with a machine learning algorithm and the predictive model was evaluated.. Result - In model performance evaluation, both artificial neural networks(ANNs) and support vector machines(SVMs) showed high accuracy of more than 91%. Conclusion - This research provides some modules of personalized adaptive learning systems that can more efficiently complete by designing a PEF system for real-time learning achievement prediction and learning motivation through an adaptive learning system based on real-time EEG analysis of learners. The implication of this initial research is to verify hypothetical situations for the development of an adaptive learning system through EEG analysis-based learning achievement prediction.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.186-187
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• 2021

Education in Korean universities is rapidly expanding to online education due to COVID-19. In response to such changes, this study proposed a means of improving the learning management system of Korean universities and analyzed the effects of using the system. The important results of this study are as follows: the learning management system was composed of 'pre-class learning,' 'team activity,' and 'participation learning' to support team-based learning. The effects that the users (instructors, learners) can obtain by adopting team-based learning and using the system were analyzed. The study concludes that for instructors, teaching work may be alleviated. For learners, it was demonstrated that they could more easily access and use data required for their education.

• The Journal of Information Systems
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• v.30 no.2
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• pp.105-126
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• 2021

Purpose The purpose of this study is to implement a optimal machine learning model about the cancellation prediction performance in car sales business. It is to apply the data set of accumulated contract, cancellation, and sales information in sales support system(SFA) which is commonly used for sales, customers and inventory management by imported car dealers, to several machine learning models and predict performance of cancellation. Design/methodology/approach This study extracts 29,073 contracts, cancellations, and sales data from 2015 to 2020 accumulated in the sales support system(SFA) for imported car dealers and uses the analysis program Python Jupiter notebook in order to perform data pre-processing, verification, and modeling that is applying and learning to Machine learning model after then the final result was predicted using new data. Findings This study confirmed that cancellation prediction is possible by applying car purchase contract information to machine learning models. It proved the possibility of developing and utilizing a generalized predictive model by using data of imported car sales system with machine learning technology. It can reduce and prevent the sales failure as caring the potential lost customer intensively and it lead to increase sales revenue by predicting the cancellation possibility of individual customers.

• The Journal of the Korea Contents Association
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• v.6 no.3
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• pp.108-115
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• 2006

e-Learning contents are composed of compounding multimedia data. It requires many professionals in contents development stage. The process of e-learning contents development can be seen as a collaborative work. In the perspective of a collaborative work process, the whole process of e-learning contents development would be regarded as collaborative work process for each participant as well as for whole group members. Most of collaborative works in contents development field are widely distributed. Members of work groups require workspaces for sharing information and communicating each other. In addition to workspaces, it also needs to support collaborative reflection such as planning for collaborative work and monitoring for work process. This paper is intended to develop the reflective collaborative work system for e-Learning contents development in order to support the systemic process of e-learning contents development. The reflective collaborative work system is composed of four supportive parts: work flow management, personal workspace, collaborative workspace, and collaborative reflection.

• Journal of the Korea Society of Computer and Information
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• v.15 no.2
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• pp.135-146
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• 2010

In this paper, we propose an Recommendation System for supporting self-directed learning on e-learning marketplace. The key idea of this system is recommendation system using revised collaborative filtering to support marketplace. Exisiting collaborative filtering method consists of 3 stages as preparing low data, building familiar customer group by selecting nearest neighbor, creating recommendation list. This study designs recommendation system to support self-directed learning by using collaborative filtering added nearest neighbor learning course that considered industry and learning level. This service helps to select right learning course to learner in industry. Recommendation System can be built by many method and to recommend the service content including explicit properties using revised collaborative filtering method can solve limitations in existing content recommendation.

• Journal of the Korean Applied Science and Technology
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• v.37 no.4
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• pp.1008-1019
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• 2020

The purpose of this study is to investigate the effects of social support of students majoring in physical education on their major satisfaction and intention to continue learning, and to prevent dropout of students majoring in physical education in the credit banking system and to find effective management methods. The research tools were verified by confirmatory factor analysis, concentration validity, discriminant validity, average variance extraction (AVE), concept reliability, and Cronbach's coefficient for validity and reliability verification of the research tools. The data processing method was conducted by using IBM SPSS Statistics 21 and IBM AMOS 21 to verify reliability analysis, correlation analysis, and structural equation model (SEM) through frequency analysis, confirmatory factor analysis, concentration validity, discriminant validity, Cronbach's coefficient calculation. The results are as follows. First, the study model was tested and the criteria were met for verifying the suitability of the relationship between social support, major satisfaction and learning persistence intention of the professors majoring in physical education in credit banking system. Second, as a result of the verification of Hypothesis 1, the social support of the professor of the physical education major in the credit banking system has a significant effect on the major satisfaction. The results of the verification of Hypothesis 2 showed that the social support of the professor of the physical education major in the credit banking system affects on the learning persistence. As a result of the verification of Hypothesis 3, it has been shown that major satisfaction has a significant effect on the learning persistence.