• Journal of Korean Society of Archives and Records Management
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• v.22 no.1
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• pp.61-82
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• 2022

This study aims to investigate the factors influencing the learning satisfaction of records management cyber education that is opened and operated by the National Archives of Korea, as well as to improve the quality of cyber education program. Cyber education consisted of an introductory course, an intensive course, and a liberal arts course. As the major factors for learning satisfaction, "validity of content structure, interaction between professors and learners, learning motivation, active learning attitude, ease of use environment, and level of organizational support" were set. An online survey was conducted on the learning satisfaction according to the curriculum of each course. The survey was conducted to 107 institutions with specialized records management personnel, and additional in-depth interviews were also conducted. The survey analysis consisted of factor analysis, independent sample T-test, analysis of variance (ANOVA), correlation analysis, and multiple regression analysis. As a result of the study, the factors influencing learning satisfaction were found in the order of interaction between professors and learners, learning motivation, and validity of content structure.

• Journal of The Korean Association For Science Education
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• v.28 no.5
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• pp.359-382
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• 2008

There are so many research literatures on conceptual change theory and the nature of concepts such as p-prims, mental model, ontological belief, and cognitive structure. Conceptual change means learning (Vosniadou, 1999; Duit;1999). It is necessary to review and elaborate existing conceptual change theories in order to explain the learning process and its implications. Therefore, we derived from reviewing literatures that learners construct new conceptual structure in response to given contexts at the same time activating their beliefs. We reviewed some mental theories that integrated cognitive and affective components and were based on framework/specific theory or information processing theory. We suggest learners' framework of conceptual structure and conflict model of conceptual structure. We expect to obtain effective ways of science teaching and learning and implications for cognitive conflict and conceptual change from using conceptual structure later.

• Journal of Information Technology Services
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• v.7 no.4
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• pp.231-246
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• 2008

This paper suggests an e-learning system model, a goal-driven personalized e-learning system, which increase the effectiveness of learning. An e-learning system following this model makes the learner choose the learning goal. The learner's choice would lead learning. Therefore, the system enables a personalized adaptive learning, which will raise the effectiveness of learning. Moreover, this paper proposes a SCORM standard, which modifies SCORM 2004 that has been insufficient to implement the "goal driven personalized e-learning system." We add a data model representing the goal that motivates learning, and propose a standard for statistics on learning objects usage. We propose each standard for contents model and sequencing information model which are parts of "goal driven personalized e-learning system." We also propose that manifest file should be added for the standard for contents model, and the file which represents the information of hierarchical structure and general learning paths should be added for the standard for sequencing information model. As a result, the system could sequence and search learning objects. We proposed an e-learning system and modified SCORM standards by considering the many factors of adaptive learning. We expect that the system enables us to optimally design personalized e-learning system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.125-129
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• 2006

By advent of NNC(Neural Network Chip), it is possible that process in parallel and discern the importance of signal with learning oneself by experience in external signal. So, the design of general purpose operation unit using VHDL(VHSIC Hardware Description Language) on the existing FPGA(Field Programmable Gate Array) can replaced EN(Expert Network) and learning algorithm. Also, neural network operation unit is possible various operation using learning of NN(Neural Network). This paper present general purpose operation unit using hierarchical structure of EN. EN of presented structure learn from logical gate which constitute a operation unit, it relocated several layer. The overall structure is hierarchical using a module, it has generality more than FPGA operation unit.

• English Language & Literature Teaching
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• v.7 no.2
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• pp.37-54
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• 2002

Some Korean speakers have trouble in learning the correct pronunciation of many complex English words which have clusters in their onset and coda position. This study shows that the difficulties Korean students have acquiring English pronunciation partly come from syllable structure differences between English and Korean. We provide an analysis based on Optimality Theory (Prince and Smolensky 1993) of the syllable structure difference and suggest that Korean speakers learn the different constraint ranking between English and Korean. This will offer Korean speakers with some helpful methods which will facilitate their learning.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.433-439
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• 1998

In this paper, we present an approach to the structure identification based on the input space partition methods and to the parameter identification by hybrid learning method in neuro-fuzzy system. The structure identification can automatically estimate the number of membership function and fuzzy rule using grid partition, tree partition, scatter partition from numerical input-output data. And then the parameter identification is carried out by the hybrid learning scheme using back-propagation and least squares estimate. Finally, we sill show its usefulness for neuro-fuzzy modeling to truck backer-upper control.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1105-1108
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• 2003

Adaptation in dynamic environments gains a significant advantage by combining evolution and learning. We propose an on-line, realtime evolutionary learning mechanism to determine the structure and the synaptic weights of a neural network controller for mobile robot navigations. We support our method, based on (1+1) evolutionary strategy, which produces changes during the lifetime of an individual to increase the adaptability of the individual itself, with a set of experiments on evolutionary neural controller for physical robots behaviors.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.677-680
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• 2000

The technique of reinforcement learning algorithm is extended to solve the multiobjective control problem for uncertain dynamic systems. A multiobjective adaptive critic structure is proposed in order to realize a max-min method in the reinforcement learning process. Also, the proposed reinforcement learning technique is applied to a multiobjective satisfactory fuzzy logic controller design in which fuzzy logic subcontrollers are assumed to be derived from human experts. Some simulation results are given in order to show effectiveness of the proposed method.

• Journal of Industrial Convergence
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• v.13 no.1
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• pp.11-23
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• 2015

Organizations use multiple team membership to enhance individual and team productivity and learning, but this structure creates competing pressures on attention and information, which make in difficult to increase both productivity and learning. My model describes how the number and variety of multiple team memberships drive different mechanisms, yielding distinct effects.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.1277-1280
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• 1998

We propose a structural learning method of MLP classifiers for a given application using PfSGA (parameter-free species genetic algorithm), which is a combining of species genetic algorithm(SGA) and parameter-free genetic algorithm(PfGA). experimental results show that PfSGA can reduce the learing time of SGA and has no influence of parameter values on structural learning. And we also convince that PfSGA is more efficient than the other methods in the aspect of misclassification ratio, learning rate, and complexity of MLP structure.