• The Journal of Korean Association of Computer Education
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• v.7 no.6
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• pp.117-128
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• 2004

Compared with traditional face-to-face instruction, online learning causes learners to experience more severe feeling of isolation and results in higher dropout rate. This is due to the lack of interaction, sense of belonging, membership, interdependency, cooperation among members and social environment that enables persistence in online learning. Therefore, it is very important for grouping e-learning community to lower the dropout rate and eliminate feeling of isolation. In this paper, the research has been done on the inclination test list to be applied for grouping the desirable learning community. And on the basis of this research, the grouping system for e-learning community(GSE) based on intelligent multi agents for an inclination test using homogeneous and heterogeneous items has been developed. GSE system has such properties that construct a personalized user profile by an agent, and then make groupings according to users' inclination. When this system was evaluated, about 88% of learners were satisfied, and they wanted the group not to be disorganized but to be maintained.

• Journal of The Korean Association of Information Education
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• v.14 no.3
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• pp.291-300
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• 2010

Group-based learning is known to be an effective means to improve scholastic achievement in online learning. Therefore, there are some previous researches for the group-based learning. A lot of previous researches define factors for grouping from the characteristics of classes, teacher's decision and students' preferences and then generate a group based on the defined factors. However, many algorithms proposed by previous researches depend on a specific class and is not a general approach since there exist several differences in terms of the need of courses, learners, and teachers. Moreover it is hard to find a automatic system for group generation. This paper proposes a grouping system which automatically generate a learner group according to characteristics of various classes. the proposed system automatically generates a learner group by using basic information for a class or additional factors inputted from a user. The proposed system defines a set of rules for learner grouping which enables automatic selection of a learner grouping algorithm tailored to the characteristics of a given class. This rule based approach allows the proposed system to accommodate various learner grouping algorithms for a later use. Also we show the usability of our system by serviceability evaluation.

• Journal of Korea Multimedia Society
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• v.8 no.4
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• pp.544-553
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• 2005

In this paper, the research has been done to develop the inclination test items to form the desirable online learning community in which social interaction may be maximized, dropout rate lowered and learners' feeling of isolation eliminated. Once developed, the inclination test items have been classified into homogeneous ones and heterogeneous ones. And on the basis of the results of this research, Intelligent agent based Inclination Test Grouping e-learning System(IIGS) has been developed, which can perform automatic grouping of online leaning community by intelligent agent. The results of this research with 1,000 teachers in reality by means of developing the grouping system have shown that 151 groups are automatically formed. Among them, 34% have shown very high degree of learning satisfaction and intended to maintain the groups in the future.

• Journal of KIISE
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• v.44 no.11
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• pp.1209-1218
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• 2017

A variety of sensors is widely used these days, and it has become much easier to acquire spatial big data streams from various sources. Since spatial data streams have inherently skewed and dynamically changing distributions, the system must effectively distribute the load among workers. Previous studies to solve this load imbalance problem are not directly applicable to processing spatial data. In this research, we propose Adaptive Spatial Key Grouping (ASKG). The main idea of ASKG is, by utilizing the previous distribution of the data streams, to adaptively suggest a new grouping scheme that evenly distributes the future load among workers. We evaluate the validity of the proposed algorithm in various environments, by conducting an experiment with real datasets while varying the number of workers, input rate, and processing overhead. Compared to two other alternative algorithms, ASKG improves the system performance in terms of load imbalance, throughput, and latency.

• Journal of Practical Engineering Education
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• v.15 no.3
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• pp.671-679
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• 2023

This study aims to establish an evaluation model by quantifying the evaluation index as a follow-up study to the development of evaluation index for work-study parallel learning companies. An evaluation model was established by verifying the 2nd level components based on the quantitative factors of the learning company, the qualitative factors, the competency factors of the person in charge, and the competency factors of the learning workers, which are the highest-level components derived from previous study. For the evaluation of a learning company, an AHP survey was conducted with experts in charge of the company consulting to derive important factors that determine the quality of on-site education and training, and the evaluation model of the learning company was completed and grouped by calculating the weight between evaluation items proceeded. Work-study parallel program was promoted as a key policy to resolve the mismatch between industrial sites and school education and realize a competency-centered society, and as of December 2022, 16,664 companies participated in the training. Learning companies play a very important role as education and training supply organizations that conduct field training. It is expected that the support and consulting plan for each level of learning companies according to the evaluation model presented in this study will be used as basic data to improve the quality of work-study parallel program.

• The Journal of the Korea Contents Association
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• v.5 no.1
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• pp.27-35
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• 2005

Recently, despite of the rapid progress of information technology in the medical and health fields, the development and management of problem sets about medical and education contents related with radiological technologist has been still achieved by manual and offline method using document editor. In this study, the unique web-based problem management system is designed and implemented. That system can efficiently manage and present various kind of problem set about integrated education and personal license without time and space limitations in order to improve the efficiency of supplementary training and to obtain the professional license for CT radiological technologist. The proposed system is composed of administration module and user module. The former supports several functions such as problem creation, problem categorization, user management, and adjustment of leveled assessment. On the other hand, the latter functions examination applying , problem retrieval, personal score retrieval, and interpretation viewing, and so on. In addition, our system is expected as a useful and practical system which provides problem interpretation and analysis of score results after applying for the examination. It can elevate ability of learning and information interchange among them preparing for CT professional radiological technologist licensing examination