• Journal of The Korean Association of Information Education
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• v.12 no.1
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• pp.99-107
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• 2008

Over online education based on wired Internet technologies, due to recent development of various mobile technologies, education based on mobile environment becomes popular. In the meanwhile, the young students are more interested in game-based education that provides more interaction and instant feedback than one-way cramming education. The purpose of this thesis is to develop a new motivation model for mobile environment and apply the model to the elementary school students. The proposed model, based on Keller's motivation model, is designed to increase study effects through motivating students with various game strategies. The proposed motivation model has the following characteristics. First of all, the best game genre can be provided for each study theme in early planning stage. Second, the model can allow students to have more interests in their study activity by providing various edutainment elements. Third, a stage of producing game synopsis and concrete scenario is included in motivation model. The stage enables more complete combination of game and mobile motivation strategies. Finally, the proposed model allows contents developed to be appled in teaching plan without any refinement. That is, the model allows a teaching plan to be extracted from study contents instantly.

• ETRI Journal
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• v.45 no.4
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• pp.713-723
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• 2023

Game bots are illegal programs that facilitate account growth and goods acquisition through continuous and automatic play. Early detection is required to minimize the damage caused by evolving game bots. In this study, we propose a game bot detection method based on action time intervals (ATIs). We observe the actions of the bots in a game and identify the most frequently occurring actions. We extract the frequency, ATI average, and ATI standard deviation for each identified action, which is to used as machine learning features. Furthermore, we measure the performance using actual logs of the Aion game to verify the validity of the proposed method. The accuracy and precision of the proposed method are 97% and 100%, respectively. Results show that the game bots can be detected early because the proposed method performs well using only data from a single day, which shows similar performance with those proposed in a previous study using the same dataset. The detection performance of the model is maintained even after 2 months of training without any revision process.

• Asia pacific journal of information systems
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• v.32 no.3
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• pp.684-705
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• 2022

A new trend has been emerging in recent years, with video game live streaming becoming a meeting ground for gamers, as well as a marketing strategy for game developers. In line with this trend, the emergence of the "Let's Play" culture has significantly changed the manner in which people enjoyed video games. In order to academically explore this new experience, this study seeks to answer the following research questions: (1) Does engaging in video game streaming offer the same feeling as playing the game? (2) If so, what are the factors that affect the feeling of telepresence from viewers' perspective? and (3) How does the feeling of telepresence affect viewers' learning experience of the streamed game? We generated and empirically tested a comprehensive research model based on the telepresence and consumer learning theories. The research findings revealed that the authenticity and pleasantness of the streamer and the interaction of viewers positively affect telepresence, which in turn is positively associated with the gained knowledge and a positive attitude toward the streamed game. Based on the research findings, various practical implications are discussed for game developers as well as platform providers.

• Journal of Korea Game Society
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• v.9 no.6
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• pp.117-125
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• 2009

This paper describes the result of applying neuro-fuzzy reasoning, which conducts Go term knowledge based on pattern knowledge, to the opening game of Go. We discuss the implementation of neuro-fuzzy reasoning for deciding the best next move to proceed through the opening game. We also let neuro-fuzzy reasoning play against TD($\lambda$) learning to test the performance. The experimental result reveals that even the simple neuro-fuzzy reasoning model can compete against TD($\lambda$) learning and it shows great potential to be applied to the real game of Go.

• Journal of the Korea Computer Industry Society
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• v.2 no.6
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• pp.759-774
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• 2001

This research has the theoretical faces of education with the internet games are based on the Networking and interactive features be caused by network focused on cooperative education, and presentation about educational game model. It includes educational features about various internet games(adventure game, simulation game). Educational game model is a definite model can realize the learning with internet game according to each stages of educational games are based on the networking.

• 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.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.236-242
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• 2003

This paper presents a cooperation strategy between a human operator and autonomous robots for an interactive robot soccer game, The interactive robot soccer game has been developed to allow humans to join into the game dynamically and reinforce entertainment characteristics. In order to make these games more interesting, a cooperation strategy between humans and autonomous robots on a team is very important. Strategies can be pre-programmed or learned by robots themselves with learning or evolving algorithms. Since the robot soccer system is hard to model and its environment changes dynamically, it is very difficult to pre-program cooperation strategies between robot agents. Q-learning - one of the most representative reinforcement learning methods - is shown to be effective for solving problems dynamically without explicit knowledge of the system. Therefore, in our research, a Q-learning based learning method has been utilized. Prior to utilizing Q-teaming, state variables describing the game situation and actions' sets of robots have been defined. After the learning process, the human operator could play the game more easily. To evaluate the usefulness of the proposed strategy, some simulations and games have been carried out.

• International Journal of Advanced Culture Technology
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• v.12 no.3
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• pp.336-344
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• 2024

The purpose of this study is to analyze the need of college students for experiential learning, collaborative learning, and enquiry-based learning. To achieve this goal, a survey was conducted with 308 college students. The need for experience, collaboration, and enquiry-based learning was comprehensively analyzed through t-tests, Borich needs analysis, and priority determination using The Locus for Focus model. The research findings are as follows: First, in Borich need analysis, the highest needs were identified for deep learning, self-directed learning, connecting theoretical knowledge with practical application, immersion, and application to real-life situations. Second, in The Locus for Focus model, the highest needs were found for abstract conceptualization, interest, conflict management, self-directed learning, and curiosity. In summary, since self-directed learning showed the highest priority simultaneously in Borich need analysis and The Locus for Focus model, it should be considered as the most prioritized item.

• Journal of Communications and Networks
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• v.18 no.3
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• pp.411-419
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• 2016

This article investigates the problem of distributed channel selection in opportunistic spectrum access networks from a perspective of interference minimization. The traditional physical (PHY)-layer interference model is for information theoretic analysis. When practical multiple access mechanisms are considered, the recently developed binary medium access control (MAC)-layer interference model in the previous work is more useful, in which the experienced interference of a user is defined as the number of competing users. However, the binary model is not accurate in mathematics analysis with poor achievable performance. Therefore, we propose a real-valued one called stochastic MAC-layer interference model, where the utility of a player is defined as a function of the aggregate weight of the stochastic interference of competing neighbors. Then, the distributed channel selection problem in the stochastic MAC-layer interference model is formulated as a weighted stochastic MAC-layer interference minimization game and we proved that the game is an exact potential game which exists one pure strategy Nash equilibrium point at least. By using the proposed stochastic learning-automata based uncoupled algorithm with heterogeneous learning parameter (SLA-H), we can achieve suboptimal convergence averagely and this result can be verified in the simulation. Moreover, the simulated results also prove that the proposed stochastic model can achieve higher throughput performance and faster convergence behavior than the binary one.

• Journal of Information Processing Systems
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• v.18 no.1
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• pp.130-145
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• 2022

Games are widely used in many fields, but not all games are successful. Then what makes games successful? The question gave us the motivation of this paper, which is to identify critical factors for successful games with topic modeling technique. It is supposed that game reviews written by experts sit on abundant insights and topics of how games succeed. To excavate these insights and topics, latent Dirichlet allocation, a topic modeling analysis technique, was used. This statistical approach provided words that implicate topics behind them. Fifty topics were inferred based on these words, and these topics were categorized by stimulation-response-desiregoal (SRDG) model, which makes a streamlined flow of how players engage in video games. This approach can provide game designers with critical factors for successful games. Furthermore, from this research result, we are going to develop a model for immersive game experiences to explain why some games are more addictive than others and how successful gamification works.