• Knowledge Management Research
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• v.2 no.1
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• pp.65-81
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• 2001

The aim of this study is classify knowledge work style management for knowledge management. It is based on the knowledge creation model of Nonaka by subdividing types of knowledge workers. It was designed to create a model for application to the actual environment of management. Nonaka suggested the process of socialization, externalization, combination, internalization that the knowledge of a person creates new knowledge through the interaction of implicit knowledge and explicit knowledge. This research demonstrated that knowledge worker of 16 forms by applying SECI model to the main function and the subordinate functions again. This study aims at achieving a higher outcome by applying the ability of existing knowledge worker to subdivided expert field efficiently. Suggested styles of knowledge worker in this research are classified into craftsman style, pragmatic style, combination style, developed style knowledge worker who creates knowledge by selecting socialization as the function and again by selecting externalization combination, internalization as subordinate functions. And they were classified into creation style, insight style, strategy style according to practical application worker and function which is selecting externalization as the main function and socialization as the subordinate functions. They were classified into future style, innovation style, analysis style, judgement style knowledge worker who are selecting combination as the main function and experiment style, intuition style, research style, learning style worker who are selecting internalization as the main function. They suggested the characteristics and cases of each type.

• Cross-Cultural Studies
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• v.37
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• pp.233-265
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• 2014

The objective of this article is to emphasize the importance of French grammar and apply effective ways in the course considering the results of investigations conducted by teachers and learners. In the first part, we observed different types of the theory of grammar teaching. The key point in choosing a theory of grammar is to adopt a learning objective defined by the level of learners. To do this, the teacher must find methods that enable learners to achieve a gradual grammatical knowledge. In the second part, we focused on the conscience of the learners et teachers in respect of the grammar's importance. Learners and teachers agreed on the importance of French grammar. However, it is essential to find effective methods that can not only attract the interest of learners but also give students the motivation towards learning French grammar. Regarding the correlation between the learning of linguistic communication and the teaching of grammar, it is very important to familiarize learners with the following facts: - The grammar is not an independent component of the other with regard to the teaching of French. - You can get a satisfactory result on learning grammar provided that it takes place in the course of linguistic communication. What we have proposed in this article is not an absolute solution to improve the course of French, with regard to learning grammar. However, we hope that this study could help to facilitate the teaching of French grammar.

• English Language & Literature Teaching
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• v.12 no.3
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• pp.267-286
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• 2006

The purpose of the study was to investigate what kinds of learning strategies EFL learners use to learn English grammar and what is benefit from structured grammar input processing. Students of the study consisted of 48 college students who took Practical English Grammar at a university in Kyung-Gi area and were divided into two groups based on grammar scores. The students were asked to take two grammar tasks and grammar tests and complete a survey including questions on grammar strategy and input processing. The results of the study are as follows. First, learners' grammar level has an effect on use of grammar attack strategy including asking teachers, using grammar books and given contexts whereas there was no significant difference between groups in the planning strategies, Among memory strategies, using grammar exercise and linking with already known structure demonstrated a significant difference between groups. Second, with regard to input processing, high level students got higher score on how much they understood the structured grammar input compared with low level students. Third, explicit implicit instruction added to input processing seems more comprehensible and more available than structured input only, Finally, it showed that there is positive relationship between perception and score of input processing tasks and grammar tests. Especially, learners' perception of input processing correlated more with final tests and tasks. Therefore, it suggests that the more input processing task need to develop and utilize in order to facilitate learners' intake.

• Korean Journal of Mathematics
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• v.23 no.1
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• pp.93-114
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• 2015

The "flipped" or "inverted" classroom, in which students study lecture-type material at home and do their "homework" in the classroom, has been the subject of research, particularly in the area of student achievement. Yet Bishop and Verleger (2013) state the need for an underlying theory to the practice. The purpose of this paper is to explore "multidimensional teaching," the authors' extension of the two-dimensional "flipped" classroom concept in light of Cambourne's (1995) Conditions for Learning. One author's math class for pre-service teachers was taught in two styles, a more traditional lecture format and in the \inverted" format. Students in the "flipped" format achieved at a higher level. Moreover, students' open-ended comments reveal that Cambourne's Conditions for Learning were implicit to the teaching practice. The authors suggest that practitioners of this style of teaching should deliberately develop student-centered practices, such as those mentioned by Cambourne, in order to retain the power that this teaching style currently has.

• The Journal of Korea Robotics Society
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• v.17 no.1
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• pp.76-85
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• 2022

One of the most fundamental challenges when designing controllers for dynamic systems is the adjustment of controller parameters. Usually the system model is used to get the initial controller, but eventually the controller parameters must be manually adjusted in the real system to achieve the best performance. To avoid this manual tuning step, data-driven methods such as machine learning were used. Recently, reinforcement learning became one alternative of this problem to be considered as an agent learns policies in large state space with trial-and-error Markov Decision Process (MDP) which is widely used in the field of robotics. However, on initial training step, as an agent tries to explore to the new state space with random action and acts directly on the controller parameters in real systems, MDP can lead the system safety-critical system failures. Therefore, the issue of 'safe exploration' became important. In this paper we meet 'safe exploration' condition with Control Barrier Function (CBF) which converts direct constraints on the state space to the implicit constraint of the control inputs. Given an initial low-performance controller, it automatically optimizes the parameters of the control law while ensuring safety by the CBF so that the agent can learn how to predict and control unknown and often stochastic environments. Simulation results on a quadrotor UAV indicate that the proposed method can safely optimize controller parameters quickly and automatically.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.5
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• pp.685-690
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• 2021

In this paper, we propose a deep learning-based transmit power control scheme to maximize the sum-rates while satisfying the minimum data-rate in downlink non-orthogonal multiple access (NOMA) systems. In downlink NOMA, we consider the co-channel interference that occurs from a base station other than the cell where the user is located, and the user feeds back the signal-to-interference plus noise power ratio (SINR) information instead of channel state information to reduce system feedback overhead. Therefore, the base station controls transmit power using only SINR information. The use of implicit SINR information has the advantage of decreasing the information dimension, but has disadvantage of reducing the data-rate. In this paper, we resolve this problem with deep learning-based training methods and show that the performance of training can be improved if the dimension of deep learning inputs is effectively reduced. Through simulation, we verify that the proposed deep learning-based power control scheme improves the sum-rate while satisfying the minimum data-rate.

• Journal of the Korean Society for information Management
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• v.41 no.1
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• pp.261-282
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• 2024

This study proposed and evaluated electroencephalography (EEG)-based and eye-tracking-based methods to determine relevance by utilizing users' implicit relevance feedback while navigating content in a digital library. For this, EEG/eye-tracking experiments were conducted on 32 participants using video, image, and text data. To assess the usefulness of the proposed methods, deep learning-based artificial intelligence (AI) techniques were used as a competitive benchmark. The evaluation results showed that EEG component-based methods (av_P600 and f_P3b components) demonstrated high classification accuracy in selecting relevant videos and images (faces/emotions). In contrast, AI-based methods, specifically object recognition and natural language processing, showed high classification accuracy for selecting images (objects) and texts (newspaper articles). Finally, guidelines for implementing a digital library interface based on EEG, eye-tracking, and artificial intelligence technologies have been proposed. Specifically, a system model based on implicit relevance feedback has been presented. Moreover, to enhance classification accuracy, methods suitable for each media type have been suggested, including EEG-based, eye-tracking-based, and AI-based approaches.

• Information Systems Review
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• v.17 no.2
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• pp.101-132
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• 2015

National technical qualifications to enhance an individual's vocational skills, the competitiveness of companies and countries have an important function to improve. Especially 'qualifications' will have a signal function to show objectively measure an individual's ability with the 'Education' The "knowledge necessary for the performance of their duties. Technology will gain knowledge about such assessment or recognition is based on certain criteria and procedures." Learning to qualify are being made through a smart learning a lot. Due to the revolution of the Internet in recent years with the development of information and communication technologies are entering into a knowledge society, the importance of information and knowledge. This contemporary smart learning education system is continuing to rapidly growing in pace with the changing time and space constraints, without teaching and learning is taking place. The purpose of this study is the ARCS motivation theory can determine a representative theory of human motivation factors and basic psychological needs dealing with the human nature of the psychological needs Interactivity and immersive learning, and to validate the empirical causality Affecting the continued use of smart learning through fun. Specifically, attention, relevance, confidence in the ARCS motivation, see their effect on the learning flow through the satisfaction we analyze empirically. Through this national technical qualifications smart learner's learning by supporting the implicit synchronization of students in learning are the degree of continued use. Therefore, to achieve the objectives of national technical qualifications and skills through a smart learning can contribute to the activation of the development and certification of course industry.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.4
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• pp.147-152
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• 2016

One of major issues in machine learning that extracts and acquires knowledge implicit in data is to find an appropriate way of representing it. Knowledge can be represented by a number of structures such as networks, trees, lists, and rules. The differences among these exist not only in their structures but also in effectiveness of the models for their problem solving capability. In this paper, we propose partition utility as a criterion function for clustering that can lead to simplification of the model and thus avoid overfitting problem. In addition, a heuristic is proposed as a way to construct balanced hierarchical models.

• Journal of the Korean Society for information Management
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• v.26 no.1
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• pp.81-105
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• 2009

In this paper, we propose user contents using behavior and location information on contents on various channels, such as web, IPTV, for contents distribution. With methods to build user and contents profiles, contents using behavior as an implicit user feedback was applied into machine learning procedure for updating user profiles and contents preference. In machine learning procedure, contents-based and collaborative filtering methods were used to analyze user's contents preference. This study proposes contents location information on web sites for final recommendation contents as well. Finally, we refer to a generalized recommender system for personalization. With those methods, more effective and accurate recommendation service can be possible.