• Journal of Korea Game Society
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• v.20 no.5
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• pp.21-30
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• 2020

The purpose of this study is to analyze the educational effect of social studies lesson in elementary school using gamification. the social studies lesson using gamification was divided into an experimental group and a control group, and then the cognitive and affective areas were evaluated and their impressions of the lesson were investigated. the experimental group students who experienced the gamification class showed significant growth and change in social studies learning academic achievement, learning motivation, learning interest, and learning efficacy compared to the control group.

• Journal of Korea Game Society
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• v.14 no.4
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• pp.95-104
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• 2014

In this research, we investigated the effect of arithmetic learning utilizing mathematical mobile application on arithmetic fluency and learning motivation of underachieving students in math. 24 4th grade math underachievers were divided into control and experimental groups. Arithmetic learning utilizing mathematical mobile application was conducted for experimental group and arithmetic learning utilizing learning worksheets was conducted for comparative group. After three weeks, the experimental group showed increase in math fluency and motivation compared to control group. Implications are discussed.

• Journal of Korea Game Society
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• v.15 no.1
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• pp.115-124
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• 2015

This research analyzed the case of Ncsoft to study the organizational learning, exploitation and exploration that create dynamic capability in hypercompetitive environment. First of all, we demonstrated the activities of exploitation and exploration in Ncsoft according to the life cycle of online game industry. An exploitation related to routine, learning and fit with existing environment brings about incremental innovation. In contrast, an exploration associated with non-learning, flexibility with changing environment results in radical innovation. We examined them based on the life cycle of its various game services. NCsoft that built the leading position in online game industry focused the exploitation activities at the stage of beginning period and growth, whereas NCsoft has increased the activities of exploration at period of mature. In addition, the firm conducts an exploration for its brand new game services and R&D. Conversely, An exploitation is conducted for sustainable updating of patch service and marketing and system building. The result implies that online game companies create sustainable competitive advantage using the balance between exploitation and exploration.

• Journal of Korea Game Society
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• v.20 no.6
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• pp.63-74
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• 2020

Recently, research on automating game tests using artificial intelligence agents instead of humans is attracting attention. This paper aims to collect play data from human and artificial intelligence and analyze their similarity as a preliminary study for game balancing automation. At this time, constraints were added at the learning stage in order to create artificial intelligence that can play similar to humans. Play datas obtained 14 people and 60 artificial intelligence by playing Flippy bird games 10 times each. The collected datas compared and analyzed for movement trajectory, action position, and dead position using the cosine similarity method. As a result of the analysis, an artificial intelligence agent with a similarity of 0.9 or more with humans was found.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.135-141
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• 2019

Recently, there has been active researches about building an artificial intelligence agent that can learn how to play a game by using re-enforcement learning. The performance of the learning can be diverse according to what kinds of deep learning activation functions they used when they train the agent. This paper compares the activation functions when we train our agent for learning how to play a 2D shooting game by using re-enforcement learning. We defined performance metrics to analyze the results and plotted them along a training time. As a result, we found ELU (Exponential Linear Unit) with a parameter 1.0 achieved best rewards than other activation functions. There was 23.6% gap between the best activation function and the worst activation function.

• 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.

• Cartoon and Animation Studies
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• s.35
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• pp.291-312
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• 2014

This study is based on the theory of R.Caillois about element of play which is motivated to infant for studying Hangul. The ultimate goal of play has to be accompanied by pleasure. And learning means permanent changes from experiences for the individual's. Play and learning, these two elements are united to the genre of serious game since the GBL (game based learning) was lead. Most importantly, in order to achieve their own Hangul learning is the fun. Coupled with fun and learning has an important issue for flow because concentration is low in infants than adults. In this case study is to know about fun factor has been applied effectively to Hangul serious mobile game. 20 Infant Hangul mobile serious games of Google Android mobile game section were selected as a case study based on more than 10,000 downloads and user's review rate by April 22, 2014. After that is currently available on the market can play a variety of cases of infant learning Hangul from previous research of R.Caillois offers four categories of play. R.Caillois of Agon, Mimicry, Alea, Ilinx have unique characteristics in comparison with its functional characteristics Hangul four are present any role in Hangul serious mobile games. As a result of the cases selected and the rules of the game will include a maximum of two of the most common types of Agon. Each attribute of the play, rather than one single factor is applied to four kinds of game play performance when properties are distributed to experience together gave the best flow. As a result of this study will be a based research for infants Hangul serious mobile game reflects the properties of the elements of a fun game that you want to combine learning.

• Journal of Korean Home Economics Education Association
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• v.6 no.2
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• pp.147-160
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• 1994

The purpose of this study was to review the literatures on designing and developing the CAI program and to develop the middle school students’CAI program for tutorial and instructional game. For these purposes, the learning and instructional theories and the developing phases and strategies for the program were reviewed to design the CAI program. And then the developing unit was selected and the CAI types and the developing direction were set according to the analysis of the CAI programs and related literatures on home economics teaching, The four phases-analysis, design, development, and formative evaluation-were carried out in this study. The results of this study are as following: 1. The CAI porgram was developed on health and food life units. The program contains 12 classes on health and food life in two floppy diskettes. It consisted of total 9,000 lines and 76 frame and takes two hours to study this program. This program could be used in educational computers an could be utilized for unit learing tutorial. It was composed of three parts-unit learning, finding maze, and finding food. In the unit learning part, the learning contents in health and food life units were structured and presented. In finding maze and food part, the basic and the applied problems were presented with game. The characteristics of this program were as followings: (1)This program was able to bring learners’motivation due to the strategies of tutorial and instructional game and they can interestingly learn the program for themselves. (2) The learner could practive the learning contents repeatedly and unit learning while playing the gaming, (3) The learner himself can review and supplement the learning contents without teacher’s help. (4) This program was developed to unit learning on health and food life, on the other hand so far many CAI programs for home economics teaching were developed for studying separate learning units. 2. To effectively utilize this program, the guide book for the student and the teacher was developed. It contained method of using the program, introduction of the program, review of the program, the program objectives, the learning contents, and the keys to progress the program.

• Educational Technology International
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• v.23 no.2
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• pp.207-237
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• 2022

As technological changes continue to accelerate every day, meeting the needs of a shifting educational landscape requires leaving an exclusively "in-person" education behind. Gamified learning environments should be carefully designed in light of conflicting studies to suit students' needs. The purpose of this meta-analysis is to draw conclusive results regarding the application of the most commonly used game elements in education, i.e., badges and leaderboards, through a comprehensive analysis of their impact on academic performance in online learning. Review Manager (RevMan 5.4) was used to analyze eligible studies selected from Emerald, SAGE, ERIC, EBSCO, and ProQuest between January 2011 and January 2022. Analyzing 37 studies found that using leaderboards and badges in online education enhanced academic performance when compared to traditional learning without gamification (SMD = 0.39). The badge-only intervention showed a larger effect size (SMD = 0.33) than the leaderboard-only intervention (SMD = 0.27). Badges and leaderboards together exhibited a larger effect size (SMD = 0.48) than individual game elements (SMD = 0.40). The impact of the game elements on academic performance was greater in the humanities (SMD = 0.51) than in STEM fields (SMD = 0.32) and was greater for K-12 students (SMD = 0.63) than for college students (SMD = 0.31). This study contributes to a timely discussion of the use of badges and leaderboards in COVID-19 online learning trends and provides relevant data for designing integrations of online education and gamification models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3867-3886
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• 2015

This article investigates the problem of opportunistic spectrum access in dynamic environment, in which the signal-to-noise ratio (SNR) is time-varying. Different from existing work on continuous feedback, we consider more practical scenarios in which the transmitter receives an Acknowledgment (ACK) if the received SNR is larger than the required threshold, and otherwise a Non-Acknowledgment (NACK). That is, the feedback is discrete. Several applications with different threshold values are also considered in this work. The channel selection problem is formulated as a non-cooperative game, and subsequently it is proved to be a potential game, which has at least one pure strategy Nash equilibrium. Following this, a multi-agent Q-learning algorithm is proposed to converge to Nash equilibria of the game. Furthermore, opportunistic spectrum access with multiple discrete feedbacks is also investigated. Finally, the simulation results verify that the proposed multi-agent Q-learning algorithm is applicable to both situations with binary feedback and multiple discrete feedbacks.