• Journal of The Korean Association of Information Education
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• v.16 no.3
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• pp.373-382
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• 2012

Of computing skills in the field of multi-media, particularly augmented reality technology contents may provide realistic learning experiences with 3D pictures through the learners' manipulation activities. However, the marker systems in the existing studies were not well developed as to maintain the students' interest and concentration. In this study, we have designed the first lesson ("Earth and Moon") of 5th graders' science with augmented reality combined system so that we could deal with manipulation activities of the relationship between augmented objects, From the experimental study, using combined augmented reality contents made a significant difference in their learning achievement and motivation. Thus augmented reality combined system can be utilized for a variety of topics to maintain students' learning motivation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.225-230
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• 2020

Machine learning is an algorithm which learns a computer based on the data so that the computer can identify the trend of the data and predict the output of new input data. Machine learning can be classified into supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is a way of learning a machine with given label of data. In other words, a method of inferring a function of the system through a pair of data and a label is used to predict a result using a function inferred about new input data. If the predicted value is continuous, regression analysis is used. If the predicted value is discrete, it is used as a classification. A result of analysis, no. 8 (5, 3.4, setosa), 27 (5, 3.4, setosa), 41 (5, 3.5, setosa), 44 (5, 3.5, setosa) and 40 (5.1, 3.4, setosa) in Table 3 were classified as the most similar Iris flower. Therefore, theoretical practical are suggested.

• (The) Korean Journal of Educational Psychology
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• v.32 no.1
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• pp.183-205
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• 2018

This study aims to demonstrate the longitudinal structural relationship between learner-centered instruction, learning motivation, growth beliefs, and academic stress. In particular, this study was carried out to focus on the structural effect of the related variables using data from the 3rd to 5th year of the Gyeonggi Education Panel Study. Results showed that while learner-centered instruction positively predicted both intrinsic and extrinsic motivation of learners, it predicted the former better. In addition, learner-centered instruction influenced academic stress through motivation, both intrinsic and extrinsic motivation were found to increase stress. Further, growth beliefs mediated motivation with learner-centered instruction; specifically, learner-centered instruction influenced learners' positive beliefs about growth, and learners who had growth beliefs had intrinsic motivation. At the same time, external motivation tended to be lower for learners who believed in the possibility of growth. Finally, the perceptions of learner-centered instruction affected academic stress through changes in growth beliefs. However, the other 3 factors (learner-centered instruction, learning motivation, and academic stress) were not statistically significant. In conclusion, learner-centered instruction was able to mitigate academic stress, demonstrating that this relationship is influenced by changes in growth beliefs rather than learning motivation, as previously studied. These results suggest that learners' perceptions and beliefs contribute to not only intrinsic motivation but also academic stress. Furthermore, it is suggested that learners need to change their learning environments in positive ways.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.261-268
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• 2021

After developing literacy education programs using coaching techniques, applying them to literacy learners, in order to see how they have an impact on improving learning achievement, we selected 13 senior literacy learners in U city and chose qualitative research method based on in-depth interviews, observation journals, and learning materials. Literature education programs using coaching techniques are a process-oriented model consisting of four stages of mind-opening, introducing positivity, strengthening learning competence and assistance, confidence and persistence. You can find the results as following. Firstly, communication between teachers and learners was expanded in the first stage, and secondly, self-directed learning ability was strengthened in the second stage by forming a positive mind. Thirdly, the results of utilizing the three-stage balanced literacy teaching method and interaction teaching method resulted in confidence in reading and writing, leading to an increase in self-efficacy. Fourthly, the fourth stage showed the results of improving learning achievement, which overcame the fear of learning with active praise and continuous encouragement and implied hope for higher courses. As a result of the above-mentioned research, I think literacy education programs using coaching techniques can be useful as an educational method for learners in the field of literacy education.

• 한국정보교육학회:학술대회논문집
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• 2008.01a
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• pp.211-216
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• 2008

프로그래밍 교육은 학습자의 창의적이고 논리적인 사고력을 함양시키고 문제해결능력을 신장시킬 수 있는 ICT 교육의 한 분야로서 많은 교육적 가치를 가지고 있다. 프로그래밍과 관련된 초등학교 교육내용은 7차 교육과정에서 누락되었지만 2005년 12월에 개정된 초 중등학교 정보통신기술 운영 지침에 따라 초등학교에도 프로그래밍 교육과정이 도입되었다. 따라서 본 논문은 5, 6학년 '정보처리의 이해' 영역의 프로그래밍에 관련된 학습내용을 학년별 연계성을 고려하여 재구성하고 학습 성취에 대한 피드백을 강화하는 프로그래밍 교육을 위한 웹 코스웨어를 설계 하였다. 웹을 기반으로 설계된 이 시스템은 학교나 가정에서의 프로그래밍 교육 기회를 확대시키는 역할을 하며, 학습자가 학습 성취도를 직접 확인하여 자신의 능력에 맞게 학습속도를 조절할 수 있어 자기 주도적 학습 능력이 향상될 것으로 기대된다. 또한 학습자에게 다양한 동기유발 자료와 학습 결과에 대한 적절한 피드백을 제공함으로써 프로그래밍 교육에 대한 흥미와 학습 성취도를 높일 것으로 기대된다.

• Electronics and Telecommunications Trends
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• v.35 no.6
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• pp.137-149
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• 2020

Several multi-agent reinforcement learning (MARL) algorithms have achieved overwhelming results in recent years. They have demonstrated their potential in solving complex problems in the field of real-time strategy online games, robotics, and autonomous vehicles. However these algorithms face many challenges when dealing with massive problem spaces in sparse reward environments. Based on the centralized training and decentralized execution (CTDE) architecture, the MARL algorithms discussed in the literature aim to solve the current challenges by formulating novel concepts of inter-agent modeling, credit assignment, multiagent communication, and the exploration-exploitation dilemma. The fundamental objective of this paper is to deliver a comprehensive survey of existing MARL algorithms based on the problem statements rather than on the technologies. We also discuss several experimental frameworks to provide insight into the use of these algorithms and to motivate some promising directions for future research.

• Journal of Korea Game Society
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• v.16 no.2
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• pp.27-40
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• 2016

Predicting stealthy behaviors plays an important role in designing stealth games. It is, however, difficult to automate this task because human players interact with dynamic environments in real time. In this paper, we present a reinforcement learning (RL) method for simulating stealthy movements in dynamic environments, in which an integrated model of Q-learning with Artificial Neural Networks (ANN) is exploited as an action classifier. Experiment results show that our simulation agent responds sensitively to dynamic situations and thus is useful for game level designer to determine various parameters for game.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.4
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• pp.399-406
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• 2020

In this paper, we propose a time-varying proportional navigation guidance law that determines the proportional navigation gain in real-time according to the operating situation. When intercepting a target, an unidentified evasion strategy causes a loss of optimality. To compensate for this problem, proper proportional navigation gain is derived at every time step by solving an optimal control problem with the inferred evader's strategy. Recently, deep reinforcement learning algorithms are introduced to deal with complex optimal control problem efficiently. We adapt the actor-critic method to build a proportional navigation gain network and the network is trained by the Proximal Policy Optimization(PPO) algorithm to learn an evasion strategy of the target. Numerical experiments show the effectiveness and optimality of the proposed method.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.4
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• pp.337-345
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• 2020

The Weapon-Target Assignment(WTA) problem can be formulated as an optimization problem that minimize the threat of targets. Existing methods consider the trade-off between optimality and execution time to meet the various mission objectives. We propose a multi-agent reinforcement learning algorithm for WTA based on mean field game to solve the problem in real-time with nearly optimal accuracy. Mean field game is a recent method introduced to relieve the curse of dimensionality in multi-agent learning algorithm. In addition, previous reinforcement learning models for WTA generally do not consider weapon interference, which may be critical in real world operations. Therefore, we modify the reward function to discourage the crossing of weapon trajectories. The feasibility of the proposed method was verified through simulation of a WTA problem with multiple targets in realtime and the proposed algorithm can assign the weapons to all targets without crossing trajectories of weapons.

• Journal of Broadcast Engineering
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• v.26 no.5
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• pp.599-607
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• 2021

Inactive region means a region filled with invalid pixel values to represent a specific image. Generally, inactive regions are occurred when the non-rectangular formatted images are converted to the rectangular shaped image, especially when 3D images are represented in 2D format. Because these inactive regions highly degrade the compression efficiency, filtering approaches are often applied to the boundaries between active and inactive regions. However, the image characteristics are not carefully considered during filtering. In the proposed method, inactive regions are padded through reinforcement learning that can consider the compression process and the image characteristics. Experimental results show that the proposed method performs an average of 3.4% better than the conventional padding method.