• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.37-44
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• 2021

This study is about a strategy to analyze learning activities in an intelligent learning system. To this end, the conceptual definition of the intelligent learning system and the type of learning using the intelligent learning system were analyzed. The learning types were presented as individual, adaptive, competency-based, and blended learning, and although there are some differences, most of them have similar characteristics. In addition, learning activity analysis is based on data such as mouse clicks, keyboarding, and uploads generated by the system. Through this, basic analysis such as viewing time and number of uploads can be performed. However, more diverse learning analysis is needed for personalization and adaptation. It can judge not only learning attitude and achievement level, but also metacognitive level and creativity level. However, since the level of metacognition includes complex human cognitive activities, the teacher's intervention is required in the judgment of the intelligent learning system.

• Journal of The Korean Association of Information Education
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• v.23 no.6
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• pp.639-653
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• 2019

The purpose of this study is to design an instructional framework and cognitive learning environment for AI education based on computational thinking in order to ground the theoretical rationale for AI education. Based on the literature review, the learning model is proposed to select the algorithms and problem-solving models through the abstraction process at the stage of data collection and discovery. Meanwhile, the instructional model of AI education through computational thinking is suggested to enhance the problem-solving ability using the AI by performing the processes of problem-solving and prediction based on the stages of automating and evaluating the selected algorithms. By analyzing the research related to the cognitive learning environment for AI education, the instructional framework was composed mainly of abstraction which is the core thinking process of computational thinking through the transition from the stage of the agency to modeling. The instructional framework of AI education and the process of constructing the cognitive learning environment presented in this study are characterized in that they are based on computational thinking, and those are expected to be the basis of further research for the instructional design of AI education.

• Journal of The Korean Association of Information Education
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• v.21 no.6
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• pp.607-618
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• 2017

Recently, there is a growing interest in learning to use games as a teaching method for digital native learners. In this study, we conducted a path analysis between bodily-kinesthetic intelligence, linguistic intelligence, flow, learning outcomes(academic achievement, persistence intention) in motion-capture game-based learning(the used game developed for elementary school history class). As a result, bodily-kinesthetic intelligence directly influenced flow and indirectly influenced learning outcomes. Linguistic intelligence did not have direct influence on flow and indirect effects on learning outcomes. Through this result, we expected that the motion-capture game-based learning facilitate learning motivation and performance of learners for higher bodily-kinesthetic intelligence.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.431-435
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• 2005

학습과 감정은 지능형 시스템을 구현하는데 있어 가장 중요한 요소이다. 본 논문에서는 강화학습을 이용하여 사용자와 상호작용을 하면서 학습을 수행하고 내부적인 감정모델을 가지고 있는 지능적인 가상 캐릭터를 구현하였다. 가상 캐릭터는 여러 가지 사물들로 이루어진 3D의 가상 환경 내에서 내부상태에 의해 자율적으로 동작하며, 또한 사용자는 가상 캐릭터에게 반복적인 명령을 통해 원하는 행동을 학습시킬 수 있다. 이러한 명령은 인공신경망을 사용하여 마우스의 제스처를 인식하여 수행할 수 있고 감정의 표현을 위해 Emotion-Mood-Personality 모델을 새로 제안하였다. 그리고 실험을 통해 사용자와 상호작용을 통한 감정의 변화를 살펴보았고 가상 캐릭터의 훈련에 따른 학습이 올바르게 수행되는 것을 확인하였다.

• Convergence Security Journal
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• v.23 no.5
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• pp.117-123
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• 2023

Recently, as the demand for Generative Artificial Intelligence (AI) and artificial intelligence has increased, the seriousness of misuse and abuse has emerged. However, intelligent CCTV, which maximizes detection of abnormal behavior, is of great help to prevent crime in the military and police. AI performs learning as taught by humans and then proceeds with self-learning. Since AI makes judgments according to the learned results, it is necessary to clearly understand the characteristics of learning. However, it is often difficult to visually judge strange and abnormal behaviors that are ambiguous even for humans to judge. It is very difficult to learn this with the eyes of artificial intelligence, and the result of learning is very many False Positive, False Negative, and True Negative. In response, this paper presented standards and methods for clarifying the learning of AI's strange and abnormal behaviors, and presented learning measures to maximize the judgment ability of intelligent CCTV's False Positive, False Negative, and True Negative. Through this paper, it is expected that the artificial intelligence engine performance of intelligent CCTV currently in use can be maximized, and the ratio of False Positive and False Negative can be minimized..

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.253-261
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• 2022

In this study, the educational effect were sought to be identified after developing and applying project learning for the artificial intelligence based liberal arts education for the non-computer majors. A paired-sample t-test was performed within each group to determine the extent of improvement in the learning flow and artificial intelligence efficacy in the experimental and control groups. After class, an independent sample t-test was performed to examine the statistical effects of pre-test and post-test on the learning flow and artificial intelligence efficacy in the experimental and control groups. The experimental group and control group demonstrated significant improvements in the learning flow and artificial intelligence efficacy before and after class, each respectively. There was no statistically significant difference in the learning flow between the experimental group for which the project learning method was applied and the control group for which only theory and practice were conducted in the artificial intelligence class. It was also confirmed that the experimental group for which the project learning method was applied improved the efficacy of artificial intelligence to a significant level compared to the control group which only proceeded with theory and practice.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.06a
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• pp.67-68
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• 2001

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.299-300
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• 2020

본 논문에서는 머신러닝, 데이터 처리 학습을 위한 EPL 기반 D.I.Y 실습 플랫폼을 통한 학생들의 학습을 통합 관리, 학습 능률 향상, 학습 흥미 유도하고 나아서 학생의 학습 패턴을 분석해 그에 적절한 강의 추천을 목표로 하는 지능형 통합 학습 관리 플랫폼을 제안한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.840-845
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• 2005

Intelligent agents to learn users' tendency have learn uscrs' tendency by sufficient users information and training time. When the intelligent agents is used in ubiquitous environment, users must wait for intelligent agents to learn, so user may be can't get proper services. In this paper we proposed an intelligent P2P mobile agent system that can learn users' tendency in real-time by sharing users' resource. The system shared users contexts on four places and made feel groups which was composed of similar users. When users' service which had the highest correlation coefficient in the peer groups was suggested, users were satisfied over $80\%$.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.693-696
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• 2021

There are various algorithms in reinforcement learning, and the algorithm used differs depending on the field. Even in games, specific algorithms are used when developing AI (artificial intelligence) using reinforcement learning. Different algorithms have different learning methods, so artificial intelligence is created differently. Therefore, the developer has to choose the appropriate algorithm to implement the AI for the purpose. To do that, the developer needs to know the algorithm's learning method and which algorithms are effective for which AI. Therefore, this paper compares the learning methods of three algorithms, SAC, PPO, and POCA, which are algorithms used to implement game AI. These algorithms are practical to apply to which types of AI implementations.