• Journal of The Korean Association of Information Education
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• v.23 no.1
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• pp.85-95
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• 2019

In this paper, we investigated the learners' interest of activity and learning contents in elementary school SW education using robot. In order to achieve the purpose of this paper, SW education using Entry and Hamster robot was conducted to 15 students who was participated in educational program in Youth training center. In addition, we examined mean difference using descriptive statistics. Our results show that students were generally interested in activities related to hamster robots. Further, interest was associated with gender. Specifically, boys were more interested in activities related to axis acceleration sensor and light sensor and girls were more interested in activities related to sound and movement. This paper was investigated students' interest and identify differences depending on gender, and supposed the concrete data that can be used in the school field.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.29-47
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• 2004

최근 로봇에 지능을 부여하기 fl한 다양한 연구가 진행되어 지고 있으며, 생물학-곤충들의 행동과 지능에 기초를 둔 로봇 지능-행동 연구가 중요한 연구 줄거리를 형성하게 되었다. 이 글은 로봇의 제어구조-숙고형, 반사행동형-에 대한 소개와 각각의 장.단점에 대한 고찰, 생물학-곤충들의 행동과 지능-에 기초를 둔 로봇'지능-행동 연구 분야의 최근의 연구 성과들을 고찰하고 소개한다. 그리고 지능을 부여하기 위한 학습 방법으로서의 강화학습(reinforcement learning)의 연구주제를 소개하고 향후의 로봇 연구 방향에 대하여 고찰한다.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.05d
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• pp.1133-1136
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• 2002

EasyLab은 다양한 부류의 학습자에게 기본적인 알고리즘 교육을 제공하여, 창의성과 논리성 계발의 효과를 거둘 수 있도록 만들어진 시스템이다. 학습자는 EasyLab을 통해 자신이 의도한 바를 알고리즘화 시켜 프로그래밍하고, 그 코드를 USB로 연결된 아바타 로봇으로 전송할 수 있다. 그러면 로봇은 프로그램화된 코드에 의해 반응을 하게 되고, 학습자는 자신의 의도와 비교, 연구, 반복할 수 있다. EasyLab은 초보자가 학습하기 쉽도록 GUI 방식을 사용하기 때문에 알고리즘 교육에 효율적으로 사용할 수 있다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.421-422
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• 2019

본 연구에서는 초등학교 영재학생의 학습지속의향을 향상시키기 위하여 로봇과 학습지속의향에 관한 관련 이론을 탐색하고, 햄스터 로봇을 활용한 프로그래밍 교육 프로그램을 설계하여 제안한다.

• The Journal of Korean Association of Computer Education
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• v.15 no.2
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• pp.57-66
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• 2012

It is the advantage of robot programming to improve the learners' interest. However, the drawback is existed in the fact that it is not easy to determine whether the problem lies in the hardware factor or programmed logic when an error takes place. This study is an attempt to develop a simulation tool which assists learners to concentrate on the problems not relating to robot assembling but to robot program. It aims to enable beginners make a sketch of their ideas and examine their logic through simulation. Therefore, an Etoys-based robot programming tool is developed to allows user to download and execute the simulated program into an assembled robot. The significance of this study is that easy activity of sketch-based simulation can support learners to understand how the principles of computer science are applied in daily life such as an automatic door system.

• Journal of The Korean Association of Information Education
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• v.19 no.1
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• pp.87-98
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• 2015

Robotics education in elementary school, It is difficult for girls to continue the motivation and willingness to learn because of a negative attitude and low recognition against the machine. In this paper, we studied method to improve the learning attitude through STEAM-based robotics education utilizing storytelling and robot smart learning system for elementary school girls. The curriculum is composed of nine themes which are selected from famous classic fairy tales for girls and we developed robot smart learning system which allows girls to enjoy robot design&control, collaborative learning, and sharing their ideas by using smart-phone. As a t-test results of learning attitude, the two groups showed statistically significant difference, the experimental group was higher average than the control group in terms of learning attitude. The robot smart learning system is effective for collaborative learning activities and maintaining learning motivation of elementary school girls.

• Journal of The Korean Association of Information Education
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• v.11 no.2
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• pp.231-241
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• 2007

With a focus on LabView language to program Lego Mindstoms Robot in afterschool class to help children develop their special ability and aptitude. The purpose of this research was to make proposal for programming learning method using a robot as an algorithm learning tool to improve creative problem solving ability. To do this, robot programming training program in the amount of 30th period and teaching aids thereof were developed, and 6th grade primary school children were taught up to 30th period, then after, they were evaluated accordingly. Results from analysis of evaluation of achievement level with a focus on outcomes according to each period revealed that learners understood most of contents of curriculum. In view of such results from evaluation, it is judged that the curriculum as well as teaching aids that devised and created have been constituted in order that school children will be able to have developed a shared understanding of their learning sufficiently, and to put it into practice easily. Through these hands-on experiences in the course of researches, researcher could have confirmed the possibility of success for robot-programming training class as new creative algorithm learning tool in the primary school curriculum.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.55-57
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• 2001

강화학습은 한 에이전트가 자신이 놓여진 환경으로부터의 보상을 최대화할 수 있는 최적의 행동 전략을 학습하는 것이다. 따라서 강화학습은 입력(상태)과 출력(행동)의 쌍으로 명확한 훈련 예들이 제공되는 교사 학습과는 다르다. 특히 Q-학습과 같은 비 모델 기반(model-free)의 강화학습은 사전에 환경에 대한 별다른 모델을 설정하거나 학습할 필요가 없으며 다양한 상태와 행동들을 충분히 자주 경험할 수만 있으면 최적의 행동전략에 도달할 수 있어 다양한 응용분야에 적용되고 있다. 하지만 실제 응용분야에서 Q-학습과 같은 강화학습이 겪는 최대의 문제는 큰 상태 공간을 갖는 문제의 경우에는 적절한 시간 내에 각 상태와 행동들에 대한 최적의 Q값에 수렴할 수 없어 효과를 거두기 어렵다는 점이다. 이런 문제점을 고려하여 본 논문에서는 로봇 축구 시뮬레이션 환경에서 각 선수 에이전트의 동적 위치 결정을 위해 효과적인 새로운 Q-학습 방법을 제안한다. 이 방법은 원래 문제의 상태공간을 몇 개의 작은 모듈들로 나누고 이들의 개별적인 Q-학습 결과를 단순히 결합하는 종래의 모듈화 Q-학습(Modular Q-Learning)을 개선하여, 보상에 끼친 각 모듈의 기여도에 따라 모듈들의 학습결과를 적응적으로 결합하는 방법이다. 이와 같은 적응적 중재에 기초한 모듈화 Q-학습법(Adaptive Mediation based Modular Q-Learning, AMMQL)은 종래의 모듈화 Q-학습법의 장점과 마찬가지로 큰 상태공간의 문제를 해결할 수 있을 뿐 아니라 보다 동적인 환경변화에 유연하게 적응하여 새로운 행동 전략을 학습할 수 있다는 장점을 추가로 가질 수 있다. 이러한 특성을 지닌 AMMQL 학습법은 로봇축구와 같이 끊임없이 실시간적으로 변화가 일어나는 다중 에이전트 환경에서 특히 높은 효과를 볼 수 있다. 본 논문에서는 AMMQL 학습방법의 개념을 소개하고, 로봇축구 에이전트의 동적 위치 결정을 위한 학습에 어떻게 이 학습방법을 적용할 수 있는지 세부 설계를 제시한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.441-446
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• 2010

Each robot decides and behaviors themselves surrounding circumstances in the swarm robot system. Robots have to conduct tasks allowed through cooperation with other robots. Therefore each robot should have the ability to learn and evolve in order to adapt to a changing environment. In this paper, we proposed learning based on Q-learning algorithm and evolutionary using Harmony Search algorithm and are trying to improve the accuracy using Harmony Search Algorithm, not the Genetic Algorithm. We verify that swarm robot has improved the ability to perform the task.

• Science of Emotion and Sensibility
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• v.27 no.2
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• pp.69-80
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• 2024

The COVID-19 pandemic highlighted the importance of remote education, yet the adoption rate of AI in the educational sector remains relatively low, and studies into learners' familiarity with using AI learning robots are scarce. In response, this study analyzes the factors influencing users' familiarity with AI learning robots in a smart learning environment tailored to the untact era. To this end, social big data analysis was used to examine changes in public perception and the frequency of mentions of smart learning and AI learning robots. The results showed that positive perceptions of smart learning significantly outweigh negative ones, reflecting the convenience and improved accessibility that technology brings to education. However, there is also a considerable negative perception attached to smartphone use, which is interpreted as reflecting concerns that smartphones may disrupt learning and bring other negative aspects of technology dependence. These results indicate mixed social concerns and expectations regarding the educational use of smart learning and AI technologies. The effective introduction and use of AI learning robots, especially in smart learning environments, necessitate considering these social perceptions. This study provides foundational data for the effective implementation and use of AI learning robots in smart learning environments and suggests the need for approaches that primarily consider users' familiarity and social perceptions in the development of educational technologies.