• Journal of the Korean earth science society
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• v.30 no.2
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• pp.257-264
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• 2009

The purpose of this study was to examine the effects of the game application learning 8th graders' scientific attitude, which was utilized as a strategy to improve the teaching skills and methods in the lesson of 'the history of the earth and diastrophism'. The subjects of this study were 120 students of 8th grade at a middle school located in a metropolitan city in Korea. To start off with homogeneity of a group, this study recruited participants by the results of a diagnostic test taken early in the year and a mid-term examination taken at the end of April. As a result, a total of 4 male classes that showed similar results on the two tests were selected and divided into two groups: one in experimental and the other in control. In addition, the top 20% students and the low 20% students were chosen for comparison of their scientific attitudes based on the results of the mid-term examination. The traditional teachings were offered to the control groups while the experimental lessons with the game activities performed at the stages of application and summary in teaching were offered to the experimental groups over 10 periods. Results of the pre- and post-test on the students' scientific attitude demonstrated that there was a statistical significance between the two groups, which suggested that the experimental group showed a meaningful improvement in the scientific attitude after experimental intervention lesson activities with game applications. Also, the more meaningful improvement in the scientific attitude was found in the lower group than in the higher group. It implies that lessons with the game activities motivated the students to voluntarily participate in school science learning by enhancing their interests. Therefore, it is suggested that game application learning be a new teaching-learning material that helps to encourage learners to actively participate in middle school science learning.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.267-277
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• 2024

This paper explores the use of machine learning in game production to create goal-oriented, realistic animations for skeleton monsters. The purpose of this research is to enhance realism by implementing intelligent movements in monsters within game development. To achieve this, we designed and implemented a learning model for skeleton monsters using reinforcement learning algorithms. During the machine learning process, various reward conditions were established, including the monster's speed, direction, leg movements, and goal contact. The use of configurable joints introduced physical constraints. The experimental method validated performance through seven statistical graphs generated using machine learning methods. The results demonstrated that the developed model allows skeleton monsters to move to their target points efficiently and with natural animation. This paper has implemented a method for creating game monster animations using machine learning, which can be applied in various gaming environments in the future. The year 2024 is expected to bring expanded innovation in the gaming industry. Currently, advancements in technology such as virtual reality, AI, and cloud computing are redefining the sector, providing new experiences and various opportunities. Innovative content optimized for this period is needed to offer new gaming experiences. A high level of interaction and realism, along with the immersion and fun it induces, must be established as the foundation for the environment in which these can be implemented. Recent advancements in AI technology are significantly impacting the gaming industry. By applying many elements necessary for game development, AI can efficiently optimize the game production environment. Through this research, We demonstrate that the application of machine learning to Unity and game engines in game development can contribute to creating more dynamic and realistic game environments. To ensure that VR gaming does not end as a mere craze, we propose new methods in this study to enhance realism and immersion, thereby increasing enjoyment for continuous user engagement.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.217-228
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• 2022

The study aimed to build an electronic strategy in innovative learning situations for the role of education in combating intellectual currents. A total of 525 Saudi university faculty members and general education teachers were surveyed using two electronic questionnaires. Arithmetic averages and standard deviations, One-way ANOVA, Scheffé's test, Pearson's correlation coefficient, and Cronbach's alpha stability coefficient were used as statistical methods. The study statistically identifies the differences between the study sample at the level of significance (0.05). and the design of a digital application for individual learning to combat deviant intellectual currents to activate them in light of Saudi Vision 2030 by combining the theoretical academic material and turning it into a learning e-game called (crosswords). The game is equipped with hyper media that supports education with entertainment to direct ideas towards the promotion of identity, the development of values towards moderation and the consolidation of intellectual security. Additionally, the learning e-game represents awareness messages in three short films to activate the role of curricula and intellectual awareness centers to apply realistically, innovatively, and effectively.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.551-561
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• 2013

The purpose of this study was firstly, to evaluate the contents of vocabulary game applications for young children's language learning. and secondly, to examine whether there is any differences between Korean and English word games in terms of the evaluation score. For this purpose, the word game applications in smart phone and tablet PC were analyzed, which included 30 Korean word games and another 30 English ones. The criteria to evaluate the contents were developed based on Children's Software Evaluation Instrument developed by CTR, the multimedia evaluation standard by Hee Sook Park, Young Joo Lee, and mobile contents evaluation standard by Soo Ui Choi. As a result, the educational value got the highest score whereas the design characteristics area got the lowest score in the whole evaluation analysis. And English word game applications mostly got higher score than Korean versions. The result of this study would suggest the way to evaluate educational game applications in use and to contribute to developing educational game contents aimed at young children's language learning.

• Journal of Korea Game Society
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• v.10 no.2
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• pp.113-121
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• 2010

This study proposes an educational game system that fit for portable internet environment as a solution to disadvantages of conventional education systems such as lack of understanding learners' learning level and one-way learning. The study analyses conventional e-Learning contents and platforms and proposes a new system adequate for high contents reusability and user-demand service. The learning contents that mainly consist of animations and games can be adjusted to learners' level, and therefore, learners can study according to various scenarios, not constrained in a fixed pattern. Our system is expected to bring much more fun to learners and the education can be conducted more effectively. To show the effectiveness of our system, an example of english pronunciation game was illustrated. As a result, the week points of the conventional e-Learning was overcame and new features of the interactivity was adopted to build a more effective educational game system.

• Journal of Information Technology Services
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• v.16 no.4
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• pp.137-148
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• 2017

Researches on the Artificial Intelligence has been explosively activated in various fields since the advent of AlphaGo. Particularly, researchers on the application of multi-layer neural network such as deep learning, and various machine learning algorithms are being focused actively. In this paper, we described a development of an artificial intelligence Janggi game based on reinforcement learning algorithm and MCTS (Monte Carlo Tree Search) algorithm with accumulated game data. The previous artificial intelligence games are mostly developed based on mini-max algorithm, which depends only on the results of the tree search algorithms. They cannot use of the real data from the games experts, nor cannot enhance the performance by learning. In this paper, we suggest our approach to overcome those limitations as follows. First, we collects Janggi expert's game data, which can reflect abundant real game results. Second, we create a graph structure by using the game data, which can remove redundant movement. And third, we apply the reinforcement learning algorithm and MCTS algorithm to select the best next move. In addition, the learned graph is stored by object serialization method to provide continuity of the game. The experiment of this study is done with two different types as follows. First, our system is confronted with other AI based system that is currently being served on the internet. Second, our system confronted with some Janggi experts who have winning records of more than 50%. Experimental results show that the rate of our system is significantly higher.

• Journal of Korea Multimedia Society
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• v.21 no.5
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• pp.626-637
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• 2018

Human exchanges information not only through words, but also through body gesture or hand gesture. And they can be used to build effective interfaces in mobile, virtual reality, and augmented reality. The past 2D gesture recognition research had information loss caused by projecting 3D information in 2D. Since the recognition of the gesture in 3D is higher than 2D space in terms of recognition range, the complexity of gesture recognition increases. In this paper, we proposed a real-time gesture recognition deep learning model and application in 3D space using deep learning technique. First, in order to recognize the gesture in the 3D space, the data collection is performed using the unity game engine to construct and acquire data. Second, input vector normalization for learning 3D gesture recognition model is processed based on deep learning. Thirdly, the SELU(Scaled Exponential Linear Unit) function is applied to the neural network's active function for faster learning and better recognition performance. The proposed system is expected to be applicable to various fields such as rehabilitation cares, game applications, and virtual reality.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.07a
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• pp.327-330
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• 2016

본 논문에서는 Unity 엔진을 이용한 학습용 게임개발의 구현 내용을 소개하였다. Unity 엔진을 이용하면 필드의 제작, 캐릭터 애니메이션 세팅, 스크립트 작성, Asset 관리, 레벨 디자인 등 많은 작업을 하나의 통합 환경에서 수행할 수 있다. 또한 컴파일 과정을 거치지 않아도 게임을 제작하는 도중 언제라도 실행해 볼 수 있기 때문에 개발에 걸리는 시간을 단축 할 수 있다. 본 연구의 과정은 게임 앱 설계 관련 프로젝트의 수행이나 학습용 게임 개발의 학습 모형을 제시한 사례로 활용할 수 있다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.171-176
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• 2020

Reinforcement learning is a well-known method for training an artificial software agent for a video game. Even though many reinforcement learning algorithms have been proposed, their performance was varies depending on an application area. This paper compares the performance of the algorithms when we train our reinforcement learning agent for a 2D racing game. We defined performance metrics to analyze the results and plotted them into various graphs. As a result, we found ACER (Actor Critic with Experience Replay) achieved the best rewards than other algorithms. There was 157% gap between ACER and the worst algorithm.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.110-113
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• 2022

Non-playable game character plays an important role in improving the concentration of the game and the interest of the user, and recently implementation of NPC with reinforcement learning has been in the spotlight. In this paper, we estimate an AI target tracking method via reinforcement learning, and implement an AI-based tracking agency of specific target object with avoiding traps through Unity ML-Agents Toolkit. The implementation is built in Unity game engine, and simulations are conducted through a number of experiments. The experimental results show that outstanding performance of the tracking target with avoiding traps is shown with good enough results.