• Journal of KIISE:Software and Applications
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• v.31 no.11
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• pp.1431-1438
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• 2004

Reinforcement learning learns policies for accomplishing a task's goal by experience through interaction between agent and environment. Q-learning, basis algorithm of reinforcement learning, has the problem of curse of dimensionality and slow learning speed in the incipient stage of learning. In order to solve the problems of Q-learning, new function approximation methods suitable for reinforcement learning should be studied. In this paper, to improve these problems, we suggest Fuzzy Q-Map algorithm that is based on online fuzzy clustering. Fuzzy Q-Map is a function approximation method suitable to reinforcement learning that can do on-line teaming and express uncertainty of environment. We made an experiment on the mountain car problem with fuzzy Q-Map, and its results show that learning speed is accelerated in the incipient stage of learning.

• Journal of Korean Home Economics Education Association
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• v.19 no.4
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• pp.119-135
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• 2007

This study attempted to explore what are the benefits that online game consumers seek in their on-line game consumption. Nowadays many on-line game consumers are extremely absorbed in the on-line game. The purpose of this study was to find the benefits that make people enjoy on-line games enthusiastically based on the Q methodology. Q methodology is a way to understand a person's structure of subjectivity. This study applied Q methodology to clarifying consumers' subjective evaluation of the on-line game benefit s. 28 Q questions were prepared through literature review, and were offered to 30 consumers. The subjects were divided into the four types seeking different benefits with on-line games: communication, enjoyment, achievement, and escapism. The results suggested that the benefits of on-line game are beyond the general characteristics of the game including human network or achievement in the on-line game community.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.672-680
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• 2003

A shopbot is a software agent whose goal is to maximize buyer´s satisfaction through automatically gathering the price and quality information of goods as well as the services from on-line sellers. In the response to shopbots´ activities, sellers on the Internet need the agents called pricebots that can help them maximize their own profits. In this paper we adopts Q-learning, one of the model-free reinforcement learning methods as a price-setting algorithm of pricebots. A Q-learned agent increases profitability and eliminates the cyclic price wars when compared with the agents using the myoptimal (myopically optimal) pricing strategy Q-teaming needs to select a sequence of state-action fairs for the convergence of Q-teaming. When the uniform random method in selecting state-action pairs is used, the number of accesses to the Q-tables to obtain the optimal Q-values is quite large. Therefore, it is not appropriate for universal on-line learning in a real world environment. This phenomenon occurs because the uniform random selection reflects the uncertainty of exploitation for the optimal policy. In this paper, we propose a Mixed Nonstationary Policy (MNP), which consists of both the auxiliary Markov process and the original Markov process. MNP tries to keep balance of exploration and exploitation in reinforcement learning. Our experiment results show that the Q-learning agent using MNP converges to the optimal Q-values about 2.6 time faster than the uniform random selection on the average.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.535-546
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• 2022

This study tried to explore factors for improving academic achievement in online non-face-to-face education based on the survey results of a University's online cooperative learning Q&A. Due to the Corona situation, both professors and learners can easily feel psychological isolation due to the implementation of all non-face-to-face online classes. As one of the methods, it was intended to suggest the direction of future education to various teachers and learners by sharing class cases in which the online cooperative learning methodology was applied. Previous studies on non-face-to-face online learning, online cooperative learning, and learning promotion method were reviewed, and the online Q&A method was adopted as a specific learning promotion method to conduct research. In the Q&A process, learners were given an opportunity to check their learning content, share knowledge and communicate, and performance evaluation-related factors such as guaranteeing anonymity of the questioner and answerer, improvement points system, and absolute evaluation were asked. As a result of the survey analysis, it was found that they are the success factors of online cooperative learning. It is a small change that can be applied in practice in the future where online non-face-to-face learning is likely to continue, but by sharing meaningful cases of application of teaching methodologies, both professors and learners being motivated and actively involved in. It is expected that we will be able to suggest methods and directions for improving skills together by changing and supplementing the learning field.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.723-724
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• 2022

지난 코로나 상황 동안 비대면 수업을 진행했고, 학생들은 빠르게 적응했다. 온라인 수업은 학습자가 이해될 때까지 반복 학습이 가능하고, 시간과 공간의 제약 없이 자기 주도적으로 학습할 수 있다는 장점이 있지만, 온라인상이라는 특징 때문에 교수자와 학습자 간 상호작용이 부족하다는 한계점이 존재한다. 하지만 이점은 차후에 비대면 수업의 지속적인 활용 및 확대를 제한하는 요인이 될 수 있다. 본 논문에서는 상호작용을 높일 수 있는 웹 기반 에듀테크 시스템을 제안한다. 온라인 수업의 강의 영상을 세부적인 내용을 나누는 Section을 통해 다른 학생들이 질문했던 Q&A 데이터를 모아서 생성된 Section-FAQ를 열람할 수 있고, 그 Q&A에 반응해서 상호작용이 가능하다. 또한 교수자에게 Q&A를 보낼 때 영상의 Section 정보와 강의시간 정보를 같이 전송하여 강의 영상을 확인하지 않고, 빠른 답변이 가능하도록 설계했다. 본 논문에서 제안하는 온라인 수업의 상호작용 향상을 위한 웹 기반 에듀테크 시스템을 통해 온라인상에서 교수자의 역할을 대신해주고 비대면 수업의 단점을 해소해주면서, 교수자과 학습자 간의 상호작용을 높여 수업의 이해도를 높이고 학습자들의 학업성취를 높일 수 있을 것이다.

• Journal of Korea Game Society
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• v.13 no.4
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• pp.91-104
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• 2013

This study aims to investigate on the perception types of online games and their characteristics. In Study 1, perceptions of online games were researched through Q methodology. From the results, the perceptions of online games are classified into four types: 'Online Game is an Obstacle of Study'; 'Online Game is Just a Game'; 'Online Game is a Stain in Life'; 'Online Game is Vaguely Bad'. Each perception type has different characteristics in terms of perception of harmfulness of games, determination of the core of game problems, and perception of a parent-child relationship. In Study 2, the distribution and characteristics of each perception type were researched through an online survey. The results show differences in an attitude to online game using features and regulations as well as in demographic parameters such as sex and age. This study holds its significance in identifying the differences between perception types of online game and providing a basic study for policy making of healthy game culture.

• Proceedings of the Korean Society for Information Management Conference
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• 2018.08a
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• pp.79-82
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• 2018

본 연구는 인터넷에서 이용할 수 있는 다양한 종류의 온라인 정보원마다 대학생들이 가지고 있는 인지적 권위가 과제정보 평가에 미치는 영향에 대해 연구하였다. 선행연구를 기반으로 도출된 변인에 따라, 온라인 학술논문, 온라인 사전, 소셜 Q&A, 온라인 사전에 대해 대학생들이 가지고 있는 각각의 인지적 권위가 과제정보의 정확성, 포괄성, 객관성 평가에 미치는 영향을 알아보았다. 분석결과, 온라인 사전, 소셜 Q&A, 온라인 사전의 인지적 권위는 과제정보의 정확성, 포괄성, 객관성 평가에 유의한 영향을 미치는 것으로 나타났으며, 온라인 학술논문의 인지적 권위는 과제정보의 정확성, 포괄성 평가에 유의한 영향을 미치는 것으로 나타났다.

• Journal of Digital Convergence
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• v.18 no.2
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• pp.183-195
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• 2020

Non-verbal communication cues seem to be increasingly used in social media, such as emoticon, like, follow, etc. A Q methodology study is conducted to explore this phenomenon. Via a series of focus group activities with Facebook heavy users, 34 Q statements concerning online non-verbal communication cues are derived. Different Facebook users conduct Q sorts of these statements based on frequent use basis. Analysis of Q sort result reveals three distinctive usage patterns of non-verbal communication cues in Facebook: 'safe distance keeping' type, 'observing hermit' type and 'fully dedicated aficionado' type. Discussions are made concerning different components of non-verbal communication cues utilized in these groups, respectively, which can provide a theoretical basis for social media design.

• Proceedings of the Korean Society for Information Management Conference
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• 2013.08a
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• pp.31-36
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• 2013

국내에서 온라인 커뮤니티는 1990년대 말 인터넷과 개인 PC의 급속한 보급으로 인해 대중화되기 시작했다. 커뮤니티는 이용자 간 활발한 커뮤니케이션을 기반으로 구축되고 있으며 이러한 커뮤니케이션은 일상적인 언어적 맥락을 바탕으로 구성된다. 이러한 커뮤니티의 내용 분석은 연구자의 직관에 의해 수행되기 마련이나 연구대상의 양이 많아질 경우 연구자의 직관만으로는 이를 분석하는데 제약이 있을 수 있다. 네트워크 분석은 이와 같은 문제점을 해결 할 수 있는 방법 중 하나이다. 본 연구는 온라인 임신 Q&A 게시판의 내용적 특성을 살펴보기 위하여 네트워크 분석을 수행하였다. 먼저 수집한 데이터의 전체적인 내용적 특성을 살펴본 후, 게시글 작성자인 임산부의 임신 시기에 따른 게시글의 내용적 특성을 살펴보았다. 분석 결과 임신시기별로 핵심적인 단어의 빈도의 차이가 있었으며 네트워크의 구성 또한 다른 것을 확인하였다. 또한 성별에 따라서도 그 내용적 특성이 다른 것을 살펴 볼 수 있었다.

• Knowledge Management Research
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• v.21 no.3
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• pp.67-87
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• 2020

Maintaining active users who repeatedly share high-quality knowledge is critical for the success of online Q&A sites. This study suggests two paths that lead to active users' continuous knowledge sharing: 1) elaborated decision process, represented by continuance intention, and 2) automated cognitive process, represented by past behavior. The direct and moderating effects of continuance intention and past behavior were verified by analyzing subjective intention data and objective behavior data of 333 active users of Naver Knowledge-iN. Using Cox proportional hazards regression and negative binomial regression, the influences of continuance intention and past behavior on two types of continuous knowledge sharing were examined. The results showed that only past behavior was significantly influential on knowledge sharing continuance and as to the frequency of knowledge sharing, both continuance intention and past behavior's influences were significant. It was also confirmed that past behavior negatively moderates continuance intention's effect on the frequency of knowledge sharing. In order to maintain active users' continuous knowledge sharing, it is important to habituate knowledge sharing through repetitive knowledge sharing behavior. And in order to increase the frequency of knowledge sharing, in addition to the habituation, appropriate benefits that can increase the continuance intention should be provided.